{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 完全连接的神经网络\n",
    "在之前的作业中，您在CIFAR-10上实现了一个完全连接的两层神经网络。实现简单，但不是很模块化，因为损失和梯度计算在一个单一的单一功能。对于一个简单的两层网络来说，这是可以管理的，但是当我们转向更大的模型时，这就变得不切实际了。理想情况下，我们希望使用更模块化的设计来构建网络，这样我们就可以独立地实现不同的层类型，然后将它们整合到具有不同架构的模型中。\n",
    "\n",
    "在本练习中，我们将使用更模块化的方法实现全连接网络。对于每一层，我们将实现“向前”和“向后”功能。“向前”函数将接收输入、权重和其他参数，并返回一个输出和一个“缓存”对象，存储后向传递所需的数据，如下所示:\n",
    "\n",
    "```python\n",
    "def layer_forward(x, w):\n",
    "  \"\"\" Receive inputs x and weights w \"\"\"\n",
    "  # Do some computations ...\n",
    "  z = # ... some intermediate value\n",
    "  # Do some more computations ...\n",
    "  out = # the output\n",
    "   \n",
    "  cache = (x, w, z, out) # Values we need to compute gradients\n",
    "   \n",
    "  return out, cache\n",
    "```\n",
    "\n",
    "反向传递将接收上游导数和“缓存”对象，并返回与输入和权重相关的梯度，如下所示:\n",
    "\n",
    "```python\n",
    "def layer_backward(dout, cache):\n",
    "  \"\"\"\n",
    "  Receive derivative of loss with respect to outputs and cache,\n",
    "  and compute derivative with respect to inputs.\n",
    "  \"\"\"\n",
    "  # Unpack cache values\n",
    "  x, w, z, out = cache\n",
    "  \n",
    "  # Use values in cache to compute derivatives\n",
    "  dx = # Derivative of loss with respect to x\n",
    "  dw = # Derivative of loss with respect to w\n",
    "  \n",
    "  return dx, dw\n",
    "```\n",
    "\n",
    "以这种方式实现了一堆层之后，我们将能够轻松地将它们组合起来，以构建具有不同架构的分类器。\n",
    "\n",
    "除了实现任意深度的全连通网络，我们还将探索不同的优化更新规则，并引入Dropout作为正则化器和Batch归一化工具来更有效地优化深度网络。\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (12.0, 9.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('X_train: ', (49000, 3, 32, 32))\n",
      "('y_train: ', (49000,))\n",
      "('X_val: ', (1000, 3, 32, 32))\n",
      "('y_val: ', (1000,))\n",
      "('X_test: ', (1000, 3, 32, 32))\n",
      "('y_test: ', (1000,))\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in list(data.items()):\n",
    "  print(('%s: ' % k, v.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 仿射层:向前\n",
    "打开文件`cs231n/layers`并实现`affine_forward`函数。\n",
    "\n",
    "一旦你做了，你可以测试你的实现通过运行以下:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_forward function:\n",
      "difference:  9.769847728806635e-10\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_forward function\n",
    "\n",
    "num_inputs = 2\n",
    "input_shape = (4, 5, 6)\n",
    "output_dim = 3\n",
    "\n",
    "input_size = num_inputs * np.prod(input_shape)\n",
    "weight_size = output_dim * np.prod(input_shape)\n",
    "\n",
    "x = np.linspace(-0.1, 0.5, num=input_size).reshape(num_inputs, *input_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=weight_size).reshape(np.prod(input_shape), output_dim)\n",
    "b = np.linspace(-0.3, 0.1, num=output_dim)\n",
    "\n",
    "out, _ = affine_forward(x, w, b)\n",
    "correct_out = np.array([[ 1.49834967,  1.70660132,  1.91485297],\n",
    "                        [ 3.25553199,  3.5141327,   3.77273342]])\n",
    "\n",
    "# Compare your output with ours. The error should be around 1e-9.\n",
    "print('Testing affine_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 仿射层:向后\n",
    "现在实现`affine_backwards函数`，并使用数值梯度检查测试您的实现。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_backward function:\n",
      "dx error:  5.399100368651805e-11\n",
      "dw error:  9.904211865398145e-11\n",
      "db error:  2.4122867568119087e-11\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_backward function\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 2, 3)\n",
    "w = np.random.randn(6, 5)\n",
    "b = np.random.randn(5)\n",
    "dout = np.random.randn(10, 5)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "_, cache = affine_forward(x, w, b)\n",
    "dx, dw, db = affine_backward(dout, cache)\n",
    "\n",
    "# The error should be around 1e-10\n",
    "print('Testing affine_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU层:前向\n",
    "在`relu_forward`函数中实现ReLU激活函数的转发，并使用以下方法测试您的实现:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_forward function:\n",
      "difference:  4.999999798022158e-08\n"
     ]
    }
   ],
   "source": [
    "# Test the relu_forward function\n",
    "\n",
    "x = np.linspace(-0.5, 0.5, num=12).reshape(3, 4)\n",
    "\n",
    "out, _ = relu_forward(x)\n",
    "correct_out = np.array([[ 0.,          0.,          0.,          0.,        ],\n",
    "                        [ 0.,          0.,          0.04545455,  0.13636364,],\n",
    "                        [ 0.22727273,  0.31818182,  0.40909091,  0.5,       ]])\n",
    "\n",
    "# Compare your output with ours. The error should be around 5e-8\n",
    "print('Testing relu_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU层:向后\n",
    "现在在`relu_back`函数中为ReLU激活函数执行反向遍历，并使用数值梯度检查来测试您的实现:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_backward function:\n",
      "dx error:  3.2756349136310288e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 10)\n",
    "dout = np.random.randn(*x.shape)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: relu_forward(x)[0], x, dout)\n",
    "\n",
    "_, cache = relu_forward(x)\n",
    "dx = relu_backward(dout, cache)\n",
    "\n",
    "# The error should be around 3e-12\n",
    "print('Testing relu_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# “三明治”层\n",
    "在神经网络中有一些常用的层模式。例如，仿射层后面经常跟一个ReLU非线性。为了简化这些常见模式，我们在文件`cs231n/layer_utils.py`中定义了几个方便的层。\n",
    "\n",
    "现在看一下` affine_relu_forward `和`affine_relu_back `函数，并运行以下代码来检查后向遍历:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_relu_forward:\n",
      "dx error:  6.750562121603446e-11\n",
      "dw error:  8.162015570444288e-11\n",
      "db error:  7.826724021458994e-12\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import affine_relu_forward, affine_relu_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 4)\n",
    "w = np.random.randn(12, 10)\n",
    "b = np.random.randn(10)\n",
    "dout = np.random.randn(2, 10)\n",
    "\n",
    "out, cache = affine_relu_forward(x, w, b)\n",
    "dx, dw, db = affine_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_relu_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_relu_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_relu_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "print('Testing affine_relu_forward:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 损失层:Softmax和SVM\n",
    "你在上次作业中实现了这些损失函数，所以我们会在这里免费给你。您仍然应该通过查看`cs231n/layers.py`中的实现来确保理解它们是如何工作的。\n",
    "\n",
    "你可以通过运行以下程序来确保实现是正确的:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing svm_loss:\n",
      "loss:  8.999602749096233\n",
      "dx error:  1.4021566006651672e-09\n",
      "\n",
      "Testing softmax_loss:\n",
      "loss:  2.302545844500738\n",
      "dx error:  9.384673161989355e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "num_classes, num_inputs = 10, 50\n",
    "x = 0.001 * np.random.randn(num_inputs, num_classes)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: svm_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = svm_loss(x, y)\n",
    "\n",
    "# Test svm_loss function. Loss should be around 9 and dx error should be 1e-9\n",
    "print('Testing svm_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: softmax_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = softmax_loss(x, y)\n",
    "\n",
    "# Test softmax_loss function. Loss should be 2.3 and dx error should be 1e-8\n",
    "print('\\nTesting softmax_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 两层网络\n",
    "在之前的作业中，您在一个单片类中实现了一个两层神经网络。现在您已经实现了必要层的模块版本，您将使用这些模块实现重新实现这两层网络。\n",
    "\n",
    "打开文件`cs231n/classifiers/fc_net`完成`TwoLayerNet`类的实现。这个类将作为在这个作业中要实现的其他网络的模型，所以请通读它以确保您理解了这个API。您可以运行下面的单元来测试您的实现。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing initialization ... \n",
      "Testing test-time forward pass ... \n",
      "Testing training loss (no regularization)\n",
      "Running numeric gradient check with reg =  0.0\n",
      "W1 relative error: 1.52e-08\n",
      "W2 relative error: 3.48e-10\n",
      "b1 relative error: 6.55e-09\n",
      "b2 relative error: 4.33e-10\n",
      "Running numeric gradient check with reg =  0.7\n",
      "W1 relative error: 8.18e-07\n",
      "W2 relative error: 7.98e-08\n",
      "b1 relative error: 1.09e-09\n",
      "b2 relative error: 7.76e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H, C = 3, 5, 50, 7\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=N)\n",
    "\n",
    "std = 1e-3\n",
    "model = TwoLayerNet(input_dim=D, hidden_dim=H, num_classes=C, weight_scale=std)\n",
    "\n",
    "print('Testing initialization ... ')\n",
    "W1_std = abs(model.params['W1'].std() - std)\n",
    "b1 = model.params['b1']\n",
    "W2_std = abs(model.params['W2'].std() - std)\n",
    "b2 = model.params['b2']\n",
    "assert W1_std < std / 10, 'First layer weights do not seem right'\n",
    "assert np.all(b1 == 0), 'First layer biases do not seem right'\n",
    "assert W2_std < std / 10, 'Second layer weights do not seem right'\n",
    "assert np.all(b2 == 0), 'Second layer biases do not seem right'\n",
    "\n",
    "print('Testing test-time forward pass ... ')\n",
    "model.params['W1'] = np.linspace(-0.7, 0.3, num=D*H).reshape(D, H)\n",
    "model.params['b1'] = np.linspace(-0.1, 0.9, num=H)\n",
    "model.params['W2'] = np.linspace(-0.3, 0.4, num=H*C).reshape(H, C)\n",
    "model.params['b2'] = np.linspace(-0.9, 0.1, num=C)\n",
    "X = np.linspace(-5.5, 4.5, num=N*D).reshape(D, N).T\n",
    "scores = model.loss(X)\n",
    "correct_scores = np.asarray(\n",
    "  [[11.53165108,  12.2917344,   13.05181771,  13.81190102,  14.57198434, 15.33206765,  16.09215096],\n",
    "   [12.05769098,  12.74614105,  13.43459113,  14.1230412,   14.81149128, 15.49994135,  16.18839143],\n",
    "   [12.58373087,  13.20054771,  13.81736455,  14.43418138,  15.05099822, 15.66781506,  16.2846319 ]])\n",
    "scores_diff = np.abs(scores - correct_scores).sum()\n",
    "assert scores_diff < 1e-6, 'Problem with test-time forward pass'\n",
    "\n",
    "print('Testing training loss (no regularization)')\n",
    "y = np.asarray([0, 5, 1])\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 3.4702243556\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with training-time loss'\n",
    "\n",
    "model.reg = 1.0\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 26.5948426952\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with regularization loss'\n",
    "\n",
    "for reg in [0.0, 0.7]:\n",
    "  print('Running numeric gradient check with reg = ', reg)\n",
    "  model.reg = reg\n",
    "  loss, grads = model.loss(X, y)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 求解器\n",
    "在之前的作业中，训练模型的逻辑与模型本身是耦合的。按照更加模块化的设计，在这次作业中，我们将训练模型的逻辑划分为单独的类。\n",
    "\n",
    "打开文件`cs231n/solver`然后通读一遍以熟悉API。这样做之后，使用一个“求解器”实例来训练一个`TwoLayerNet`，它在验证集上达到至少“50%”的精度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 4900) loss: 2.304060\n",
      "(Epoch 0 / 10) train acc: 0.116000; val_acc: 0.094000\n",
      "(Iteration 101 / 4900) loss: 1.829613\n",
      "(Iteration 201 / 4900) loss: 1.857390\n",
      "(Iteration 301 / 4900) loss: 1.744448\n",
      "(Iteration 401 / 4900) loss: 1.420187\n",
      "(Epoch 1 / 10) train acc: 0.407000; val_acc: 0.422000\n",
      "(Iteration 501 / 4900) loss: 1.565913\n",
      "(Iteration 601 / 4900) loss: 1.700510\n",
      "(Iteration 701 / 4900) loss: 1.732213\n",
      "(Iteration 801 / 4900) loss: 1.688361\n",
      "(Iteration 901 / 4900) loss: 1.439529\n",
      "(Epoch 2 / 10) train acc: 0.497000; val_acc: 0.468000\n",
      "(Iteration 1001 / 4900) loss: 1.385772\n",
      "(Iteration 1101 / 4900) loss: 1.278401\n",
      "(Iteration 1201 / 4900) loss: 1.641580\n",
      "(Iteration 1301 / 4900) loss: 1.438847\n",
      "(Iteration 1401 / 4900) loss: 1.172536\n",
      "(Epoch 3 / 10) train acc: 0.490000; val_acc: 0.466000\n",
      "(Iteration 1501 / 4900) loss: 1.346286\n",
      "(Iteration 1601 / 4900) loss: 1.268492\n",
      "(Iteration 1701 / 4900) loss: 1.318215\n",
      "(Iteration 1801 / 4900) loss: 1.395750\n",
      "(Iteration 1901 / 4900) loss: 1.338233\n",
      "(Epoch 4 / 10) train acc: 0.532000; val_acc: 0.497000\n",
      "(Iteration 2001 / 4900) loss: 1.343165\n",
      "(Iteration 2101 / 4900) loss: 1.393173\n",
      "(Iteration 2201 / 4900) loss: 1.276734\n",
      "(Iteration 2301 / 4900) loss: 1.287951\n",
      "(Iteration 2401 / 4900) loss: 1.352778\n",
      "(Epoch 5 / 10) train acc: 0.525000; val_acc: 0.475000\n",
      "(Iteration 2501 / 4900) loss: 1.390234\n",
      "(Iteration 2601 / 4900) loss: 1.276361\n",
      "(Iteration 2701 / 4900) loss: 1.111768\n",
      "(Iteration 2801 / 4900) loss: 1.271688\n",
      "(Iteration 2901 / 4900) loss: 1.272039\n",
      "(Epoch 6 / 10) train acc: 0.546000; val_acc: 0.509000\n",
      "(Iteration 3001 / 4900) loss: 1.304489\n",
      "(Iteration 3101 / 4900) loss: 1.346667\n",
      "(Iteration 3201 / 4900) loss: 1.325510\n",
      "(Iteration 3301 / 4900) loss: 1.392728\n",
      "(Iteration 3401 / 4900) loss: 1.402001\n",
      "(Epoch 7 / 10) train acc: 0.567000; val_acc: 0.505000\n",
      "(Iteration 3501 / 4900) loss: 1.319024\n",
      "(Iteration 3601 / 4900) loss: 1.153287\n",
      "(Iteration 3701 / 4900) loss: 1.180922\n",
      "(Iteration 3801 / 4900) loss: 1.093164\n",
      "(Iteration 3901 / 4900) loss: 1.135902\n",
      "(Epoch 8 / 10) train acc: 0.568000; val_acc: 0.490000\n",
      "(Iteration 4001 / 4900) loss: 1.191735\n",
      "(Iteration 4101 / 4900) loss: 1.359396\n",
      "(Iteration 4201 / 4900) loss: 1.227283\n",
      "(Iteration 4301 / 4900) loss: 1.024113\n",
      "(Iteration 4401 / 4900) loss: 1.327583\n",
      "(Epoch 9 / 10) train acc: 0.592000; val_acc: 0.504000\n",
      "(Iteration 4501 / 4900) loss: 0.963330\n",
      "(Iteration 4601 / 4900) loss: 1.445619\n",
      "(Iteration 4701 / 4900) loss: 1.007542\n",
      "(Iteration 4801 / 4900) loss: 1.005175\n",
      "(Epoch 10 / 10) train acc: 0.611000; val_acc: 0.512000\n"
     ]
    }
   ],
   "source": [
    "model = TwoLayerNet()\n",
    "solver = None\n",
    "\n",
    "##############################################################################\n",
    "# TODO: Use a Solver instance to train a TwoLayerNet that achieves at least  #\n",
    "# 50% accuracy on the validation set.                                        #\n",
    "##############################################################################\n",
    "solver = Solver(model, data,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                },\n",
    "                lr_decay=0.95,\n",
    "                num_epochs=10, batch_size=100,\n",
    "                print_every=100)\n",
    "solver.train()\n",
    "##############################################################################\n",
    "#                             END OF YOUR CODE                               #\n",
    "##############################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell to visualize training loss and train / val accuracy\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.title('Accuracy')\n",
    "plt.plot(solver.train_acc_history, '-o', label='train')\n",
    "plt.plot(solver.val_acc_history, '-o', label='val')\n",
    "plt.plot([0.5] * len(solver.val_acc_history), 'k--')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(15, 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 多层网络\n",
    "接下来，您将实现一个具有任意数量隐藏层的全连接网络。\n",
    "\n",
    "读取文件`cs231n/classifier /fc_net.py`中的`FullyConnectedNet`类。\n",
    "\n",
    "实现初始化、向前传递和向后传递。目前，不必担心实现dropout或批处理标准化;我们将很快添加这些功能。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 初始损失和梯度检查"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "作为一种完整性检查，运行以下命令来检查初始损失，并对有正则化和无正则化的网络进行梯度检查。最初的损失合理吗?\n",
    "\n",
    "对于梯度检查，您应该期望在1e-6或更少的地方看到错误。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.3004790897684924\n",
      "W1 relative error: 1.48e-07\n",
      "W2 relative error: 2.21e-05\n",
      "W3 relative error: 3.53e-07\n",
      "b1 relative error: 5.38e-09\n",
      "b2 relative error: 2.09e-09\n",
      "b3 relative error: 5.80e-11\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  7.052114776533016\n",
      "W1 relative error: 7.36e-09\n",
      "W2 relative error: 6.87e-08\n",
      "W3 relative error: 3.48e-08\n",
      "b1 relative error: 1.48e-08\n",
      "b2 relative error: 1.72e-09\n",
      "b3 relative error: 1.80e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "  print('Running check with reg = ', reg)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64)\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "作为另一个完整性检查，请确保您可以过度适合50个图像的小数据集。首先，我们将尝试一个三层网络，每层100个单位。您将需要调整学习速率和初始化规模，但您应该能够在20个epoch内超额拟合并实现100%的训练精度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 357.428290\n",
      "(Epoch 0 / 20) train acc: 0.140000; val_acc: 0.111000\n",
      "(Epoch 1 / 20) train acc: 0.120000; val_acc: 0.111000\n",
      "(Epoch 2 / 20) train acc: 0.100000; val_acc: 0.081000\n",
      "(Epoch 3 / 20) train acc: 0.040000; val_acc: 0.097000\n",
      "(Epoch 4 / 20) train acc: 0.120000; val_acc: 0.105000\n",
      "(Epoch 5 / 20) train acc: 0.160000; val_acc: 0.107000\n",
      "(Iteration 11 / 40) loss: 4062748033166512669146890481179922634050366887028969624284570245514046405899939850369081499364981021366493904896.000000\n",
      "(Epoch 6 / 20) train acc: 0.120000; val_acc: 0.119000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hanlu\\Desktop\\CS231n\\assignment2\\cs231n\\layers.py:686: RuntimeWarning: invalid value encountered in subtract\n",
      "  shifted_logits = x - np.max(x, axis=1, keepdims=True)\n",
      "C:\\Users\\hanlu\\Desktop\\CS231n\\assignment2\\cs231n\\classifiers\\fc_net.py:302: RuntimeWarning: overflow encountered in multiply\n",
      "  self.params['W' + str(self.num_layers)] * self.params['W' + str(self.num_layers)])\n",
      "C:\\Users\\hanlu\\Desktop\\CS231n\\assignment2\\cs231n\\classifiers\\fc_net.py:302: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  self.params['W' + str(self.num_layers)] * self.params['W' + str(self.num_layers)])\n",
      "C:\\Users\\hanlu\\Desktop\\CS231n\\assignment2\\cs231n\\classifiers\\fc_net.py:311: RuntimeWarning: overflow encountered in multiply\n",
      "  loss += 0.5 * self.reg * np.sum(self.params['W' + str(i)] * self.params['W' + str(i)])\n",
      "C:\\Users\\hanlu\\Desktop\\CS231n\\assignment2\\cs231n\\classifiers\\fc_net.py:311: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  loss += 0.5 * self.reg * np.sum(self.params['W' + str(i)] * self.params['W' + str(i)])\n",
      "C:\\Users\\hanlu\\Desktop\\CS231n\\assignment2\\cs231n\\layers.py:109: RuntimeWarning: invalid value encountered in greater\n",
      "  dx = (x > 0) * dout\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Epoch 7 / 20) train acc: 0.080000; val_acc: 0.087000\n",
      "(Epoch 8 / 20) train acc: 0.080000; val_acc: 0.087000\n",
      "(Epoch 9 / 20) train acc: 0.080000; val_acc: 0.087000\n",
      "(Epoch 10 / 20) train acc: 0.080000; val_acc: 0.087000\n",
      "(Iteration 21 / 40) loss: nan\n",
      "(Epoch 11 / 20) train acc: 0.080000; val_acc: 0.087000\n",
      "(Epoch 12 / 20) train acc: 0.080000; val_acc: 0.087000\n",
      "(Epoch 13 / 20) train acc: 0.080000; val_acc: 0.087000\n",
      "(Epoch 14 / 20) train acc: 0.080000; val_acc: 0.087000\n",
      "(Epoch 15 / 20) train acc: 0.080000; val_acc: 0.087000\n",
      "(Iteration 31 / 40) loss: nan\n",
      "(Epoch 16 / 20) train acc: 0.080000; val_acc: 0.087000\n",
      "(Epoch 17 / 20) train acc: 0.080000; val_acc: 0.087000\n",
      "(Epoch 18 / 20) train acc: 0.080000; val_acc: 0.087000\n",
      "(Epoch 19 / 20) train acc: 0.080000; val_acc: 0.087000\n",
      "(Epoch 20 / 20) train acc: 0.080000; val_acc: 0.087000\n",
      "==================================================\n",
      "weight_scale 1.000000e-01 learning_rate 1.000000e-02 \n"
     ]
    },
    {
     "data": {
      "image/png": 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c5vnyvGYCAICNMM/TOR6S5KlJzq+qc6dlL05yVJJ096uTPDHJs6rqhiTXJTmxu3uOMwEAwC02t4ju7o8kqb1s84okr5jXDAAAMA++sRAAAAaJaAAAGCSiAQBgkIgGAIBBIhoAAAaJaAAAGCSiAQBgkIgGAIBBIhoAAAaJaAAAGCSiAQBgkIgGAIBBIhoAAAaJaAAAGCSiAQBgkIgGAIBBIhoAAAaJaAAAGCSiAQBgkIgGAIBBIhoAAAaJaAAAGCSiAQBgkIgGAIBBIhoAAAaJaAAAGCSiAQBgkIgGAIBBIhoAAAaJaAAAGCSiAQBgkIgGAIBBIhoAAAaJaAAAGCSiAQBgkIgGAIBBIhoAAAaJaAAAGCSiAQBgkIgGAIBBIhoAAAaJaAAAGCSiAQBgkIgGAIBBIhoAAAaJaAAAGCSiAQBgkIgGAIBBIhoAAAaJaAAAGCSiAQBgkIgGAIBBIhoAAAaJaAAAGCSiAQBgkIgGAIBBc4voqjqyqj5QVRdW1QVV9dw1tqmq+t2quriqzquq+89rHgAA2CgHzfG5b0jy/O4+p6pun+TsqnpPd39mxTaPSXL09PODSV41/QYAgKU1tyPR3X15d58z3f5KkguTHL5qs8cn+eOe+ViSQ6vqLvOaCQAANsI+OSe6qrYlOTbJWatWHZ7kkhX3d+bbQztVdVJV7aiqHbt27ZrXmAAAsC5zj+iqul2StyX5+e6+ZvXqNR7S37ag+9Tu3t7d27du3TqPMQEAYN3mGtFVdXBmAf3G7j59jU12Jjlyxf0jklw2z5kAAOCWmufVOSrJa5Jc2N0v381mZyR52nSVjgclubq7L5/XTAAAsBHmeXWOhyR5apLzq+rcadmLkxyVJN396iTvSvLYJBcn+WqSn5jjPAAAsCHmFtHd/ZGsfc7zym06yc/OawYAAJiHodM5ptMubjuvYQAAYH+w14iuqj+uqjtU1XcmuSDJ56vqefMfDQAAltN6jkT/2+nSdE9I8peZXUHjGfMcCgAAltl6IvpWVXVQZt8u+Pbu/nqSb8x3LAAAWF7rieg/TPKPSb4ryV9V1VFJrp3rVAAAsMT2GtHd/dvdfdfuftR0NY1Lkjx8/qMBAMByWs8HC59dVXeYbv9+krOSPHTegwEAwLJaz+kcJ3X3NVX1qCSHJ3lWkt+c71gAALC81hPRPf1+TJI/6u6z1/k4AADYlNYTw5+qqncl+dEk766q2+VfwxoAAA446/na759I8oAkF3f3V6vqsCTPnO9YAACwvPYa0d194xTO/6mqkuSvuvvdc58MAACW1HquzvHfkrwgyeemn5Or6tfnPRgAACyr9ZzO8aNJ7t/dNyRJVb02yTlJfmmegwEAwLJa71U2br+b2wAAcMBZz5Ho30xyTlW9L0klOS7JS+Y5FAAALLP1fLDwDVX1gSQ/mFlEv6S7L537ZAAAsKR2G9FVdZ9Viy6eft+pqu7U3efNbywAAFheezoS/co9rOskD9vgWQAAYL+w24ju7ofuy0EAAGB/sd6rcwAAABMRDQAAg0Q0AAAM2usl7ta4SkeSXJ3kku7+xsaPBAAAy209X7bymiT3S3JBZteJvmeSTye5Y1Wd1N3vm+N8AACwdNZzOsffJnlAd9+vu++b5AFJzk3y6CT/c57DAQDAMlpPRN9z5RerdPf5Se7f3Rfv4TEAALBpred0jr+rqt9L8ubp/pOSXFxVt05yw9wmAwCAJbWeI9FPS7IzyQuTvCjJZUmenllAP2J+owEAwHLa65Ho7v5qkt+Yfla7esMnAgCAJbeeS9w9KMmvJLnbyu27+/vnOBcAACyt9ZwT/UdJXpDk7CQ3znccAABYfuuJ6Gu6+x1znwQAAPYT64no91fV/0hyepKv3bRw5WXvAADgQLKeiP6hVb+TpJM8bOPHAQCA5beeq3M8dF8MAgAA+4vdRnRVPbm731RVz1lrfXf/7vzGAgCA5bWnI9HfNf3eui8GAQCA/cVuI7q7/9f0+5f33TgAALD81vNlK4cl+ckk2/KtX7Zy0vzGAgCA5bWeq3P8eZKPJflIfNkKAACsK6Jv293Pn/skAACwn/iOdWzz7qp61NwnAQCA/cR6IvpnkvxFVV1bVVdW1T9V1ZXzHgwAAJbVek7nOGzuUwAAwH5kT1+2cnR3/22Se+1mk/PmMxIAACy3PR2JfmGSZyZ55RrrOsnD5jIRAAAsuT192cozp98P3XfjAADA8lvPOdGpqnsk+YEkt7lpWXf/6byGAgCAZbaebyz8pSSPSnKPJGcmeXRmX7wiogEAOCCt5xJ3T0ryH5Jc3t1PTXLfrPMINgAAbEbriejruvvGJDdU1e2TfCHJ98x3LAAAWF7rOaL8yao6NMlrk+xIck2Sc+Y6FQAALLE9RnRVVZJf7e6rkryyqs5McofuFtEAAByw9ng6R3d3kneuuH+xgAYA4EC3nnOiP15V9x994qp6bVVdUVWf3s3646rq6qo6d/p5yehrAADAIuzpa78P6u4bkvxQkv+jqv4uyT8nqcwOUu8trF+X5BVJ/ngP23y4ux83NjIAACzWns6J/niS+yd5ws154u7+UFVtuzmPBQCAZbaniK4k6e6/m+PrP7iqPpXksiS/2N0XzPG1AABgQ+wpordW1fN2t7K7X34LX/ucJHfr7mur6rFJ3p7k6LU2rKqTkpyUJEcdddQtfFkAALhl9vTBwi1Jbpfk9rv5uUW6+5ruvna6/a4kB1fVYbvZ9tTu3t7d27du3XpLXxoAAG6RPR2Jvry7XzqvF66qOyf5Ynd3VT0ws6D/8rxeDwAANspez4m+uarqTUmOS3JYVe1M8itJDk6S7n51kicmeVZV3ZDkuiQnTtelBgCApbaniH7ELXni7n7yXta/IrNL4AEAwH5lt+dEd/eV+3IQAADYX6znGwsBAIAVRDQAAAwS0QAAMEhEAwDAIBENAACDRDQAAAwS0QAAMEhEAwDAIBENAACDRDQAAAwS0QAAMEhEAwDAIBENAACDRDQAAAwS0QAAMEhEAwDAIBENAACDRDQAAAwS0QAAMEhEAwDAIBENAACDRDQAAAwS0QAAMEhEAwDAIBENAACDRDQAAAwS0QAAMEhEAwDAIBENAACDRDQAAAwS0QAAMEhEAwDAIBENAACDRDQAAAwS0QAAMEhEAwDAIBENAACDRDQAAAwS0QAAMEhEAwDAIBENAACDRDQAAAwS0QAAMEhEAwDAIBENAACDRDQAAAwS0QAAMEhEAwDAIBENAACDRDQAAAwS0QAAMEhEAwDAIBENAACDRDQAAAwS0QAAMGhuEV1Vr62qK6rq07tZX1X1u1V1cVWdV1X3n9csAACwkeZ5JPp1SY7fw/rHJDl6+jkpyavmOAsAAGyYuUV0d38oyZV72OTxSf64Zz6W5NCqusu85gEAgI2yyHOiD09yyYr7O6dl36aqTqqqHVW1Y9euXftkOAAA2J1FRnStsazX2rC7T+3u7d29fevWrXMeCwAA9myREb0zyZEr7h+R5LIFzQIAAOu2yIg+I8nTpqt0PCjJ1d19+QLnAQCAdTloXk9cVW9KclySw6pqZ5JfSXJwknT3q5O8K8ljk1yc5KtJfmJeswAAwEaaW0R395P3sr6T/Oy8Xh8AAObFNxYCAMAgEQ0AAINENAAADBLRAAAwSEQDAMAgEQ0AAINENAAADBLRAAAwSEQDAMAgEQ0AAINENAAADBLRAAAwSEQDAMAgEQ0AAINENAAADBLRAAAwSEQDAMAgEQ0AAINENAAADBLRAAAwSEQDAMAgEQ0AAINENAAADBLRAAAwSEQDAMAgEQ0AAINENAAADBLRAAAwSEQDAMAgEQ0AAINENAAADBLRAAAwSEQDAMAgEQ0AAINENAAADBLRAAAwSEQDAMAgEQ0AAINENAAADBLRAAAwSEQDAMAgEQ0AAINENAAADBLRAAAwSEQDAMAgEQ0AAINENAAADBLRAAAwSEQDAMAgEQ0AAINENAAADBLRAAAwSEQDAMAgEQ0AAINENAAADBLRAAAwaK4RXVXHV9VFVXVxVb1wjfXPqKpdVXXu9PNT85wHAAA2wkHzeuKq2pLklUl+JMnOJJ+oqjO6+zOrNn1Ldz97XnMAAMBGm+eR6Acmubi7P9fdX0/y5iSPn+PrAQDAPjHPiD48ySUr7u+clq32Y1V1XlWdVlVHrvVEVXVSVe2oqh27du2ax6wAALBu84zoWmNZr7r/jiTbuvs+Sd6b5PVrPVF3n9rd27t7+9atWzd4TAAAGDPPiN6ZZOWR5SOSXLZyg+7+cnd/bbr7B0keMMd5AABgQ8wzoj+R5OiquntV3SrJiUnOWLlBVd1lxd0Tklw4x3kAAGBDzO3qHN19Q1U9O8mZSbYkeW13X1BVL02yo7vPSPKcqjohyQ1JrkzyjHnNAwAAG6W6V5+mvNy2b9/eO3bsWPQYAABsclV1dndvX2udbywEAIBBIhoAAAaJaAAAGCSiAQBgkIgGAIBBIhoAAAaJaAAAGCSiAQBgkIgGAIBBIhoAAAaJaAAAGCSiAQBgkIgGAIBBIhoAAAaJaAAAGCSiAQBgkIgGAIBBIhoAAAaJaAAAGCSiAQBgkIgGAIBBIhoAAAaJaAAAGCSiAQBgkIgGAIBBIhoAAAaJaAAAGCSiAQBgkIgGAIBBIhoAAAaJaAAAGCSiAQBgkIgGAIBBIhoAAAaJaAAAGCSiAQBgkIgGAIBBIhoAAAaJaAAAGCSiAQBgkIgGAIBBIhoAAAaJaAAAGCSiAQBgkIgGAIBBIhoAAAaJaAAAGCSiAQBgkIgGAIBBIhoAAAaJaAAAGCSiAQBgkIgGAIBBIhoAAAaJaAAAGDTXiK6q46vqoqq6uKpeuMb6W1fVW6b1Z1XVtnnOAwAAG+GgeT1xVW1J8sokP5JkZ5JPVNUZ3f2ZFZs9M8k/dff3VdWJSX4jyZPmNdPN9fZPXppTzrwol111Xe566CE5+dHH5AnHHr7osYZtlv1I7Muy2iz7sln2I7Evy2iz7EdiX5bVZtqXZTa3iE7ywCQXd/fnkqSq3pzk8UlWRvTjk/zqdPu0JK+oqurunuNcQ97+yUvzotPPz3XX35gkufSq6/Ki089Pkv3qH8jNsh+JfVlWm2VfNst+JPZlGW2W/Ujsy7LaTPuy7OZ5OsfhSS5ZcX/ntGzNbbr7hiRXJ7nTHGcadsqZF33zH8SbXHf9jTnlzIsWNNHNs1n2I7Evy2qz7Mtm2Y/EviyjzbIfiX1ZVptpX5bdPCO61li2+gjzerZJVZ1UVTuqaseuXbs2ZLj1uuyq64aWL6vNsh+JfVlWm2VfNst+JPZlGW2W/Ujsy7LaTPuy7OYZ0TuTHLni/hFJLtvdNlV1UJI7Jrly9RN196ndvb27t2/dunVO467troceMrR8WW2W/Ujsy7LaLPuyWfYjsS/LaLPsR2JfltVm2pdlN8+I/kSSo6vq7lV1qyQnJjlj1TZnJHn6dPuJSd6/TOdDJ8nJjz4mhxy85VuWHXLwlpz86GMWNNHNs1n2I7Evy2qz7Mtm2Y/EviyjzbIfiX1ZVptpX5bd3D5Y2N03VNWzk5yZZEuS13b3BVX10iQ7uvuMJK9J8idVdXFmR6BPnNc8N9dNJ+Hv759y3Sz7kdiXZbVZ9mWz7EdiX5bRZtmPxL4sq820L8uuluzA715t3769d+zYsegxAADY5Krq7O7evtY631gIAACDRDQAAAwS0QAAMEhEAwDAIBENAACDRDQAAAwS0QAAMEhEAwDAIBENAACDRDQAAAwS0QAAMEhEAwDAIBENAACDRDQAAAwS0QAAMEhEAwDAIBENAACDqrsXPcOQqtqV5B8W9PKHJfnSgl6btXlPlpP3Zfl4T5aP92Q5eV+WzyLfk7t199a1Vux3Eb1IVbWju7cveg7+lfdkOXlflo/3ZPl4T5aT92X5LOt74nQOAAAYJKIBAGCQiB5z6qIH4Nt4T5aT92X5eE+Wj/dkOXlfls9SvifOiQYAgEGORAMAwCARvQ5VdXxVXVRVF1fVCxc9D0lVHVlVH6iqC6vqgqp67qJnYqaqtlTVJ6vqnYuehZmqOrSqTquqz07/n3nwomc60FXVL0x/dn26qt5UVbdZ9EwHoqp6bVVdUVWfXmfaS74AAAVuSURBVLHs31TVe6rqb6ff37XIGQ80u3lPTpn+/Dqvqv6sqg5d5Iw3EdF7UVVbkrwyyWOS/ECSJ1fVDyx2KpLckOT53X3PJA9K8rPel6Xx3CQXLnoIvsXvJPmL7r5HkvvG+7NQVXV4kuck2d7d906yJcmJi53qgPW6JMevWvbCJO/r7qOTvG+6z77zunz7e/KeJPfu7vsk+ZskL9rXQ61FRO/dA5Nc3N2f6+6vJ3lzkscveKYDXndf3t3nTLe/klkUHL7YqaiqI5L8xyR/uOhZmKmqOyR5WJLXJEl3f727r1rsVCQ5KMkhVXVQku9MctmC5zkgdfeHkly5avHjk7x+uv36JE/Yp0Md4NZ6T7r7L7v7hunux5Icsc8HW4OI3rvDk1yy4v7OiLWlUlXbkhyb5KzFTkKS/zvJC5J8Y9GD8E3fk2RXkj+aTrP5w6q67aKHOpB196VJfivJPya5PMnV3f2Xi52KFf637r48mR2wSfLdC56Hb/WTSd696CESEb0etcYylzRZElV1uyRvS/Lz3X3Nouc5kFXV45Jc0d1nL3oWvsVBSe6f5FXdfWySf46/nl6o6Rzbxye5e5K7JrltVT1lsVPB8quq/zOz0znfuOhZEhG9HjuTHLni/hHx125LoaoOziyg39jdpy96HvKQJCdU1d9ndtrTw6vqDYsdicz+DNvZ3Tf9Tc1pmUU1i/PIJJ/v7l3dfX2S05P8+wXPxL/6YlXdJUmm31cseB6SVNXTkzwuyX/pJbk+s4jeu08kObqq7l5Vt8rswx9nLHimA15VVWbneF7Y3S9f9Dwk3f2i7j6iu7dl9v+T93e3o2sL1t1fSHJJVR0zLXpEks8scCRmp3E8qKq+c/qz7BHxYc9lckaSp0+3n57kzxc4C5ldJS3Jf01yQnd/ddHz3ERE78V0Ivuzk5yZ2R9yb+3uCxY7FZkd9XxqZkc7z51+HrvooWBJ/VySN1bVeUnul+S/L3ieA9r0twKnJTknyfmZ/bt4Kb+RbbOrqjcl+eskx1TVzqp6ZpKXJfmRqvrbJD8y3Wcf2c178ookt0/ynunf969e6JAT31gIAACDHIkGAIBBIhoAAAaJaAAAGCSiAQBgkIgGAIBBIhpgCVXVtdPvbVX1nzf4uV+86v7/t5HPD3AgENEAy21bkqGIrqote9nkWyK6u31bHsAgEQ2w3F6W5KHTFwz8QlVtqapTquoTVXVeVf10klTVcVX1gar608y+wCNV9faqOruqLqiqk6ZlL0tyyPR8b5yW3XTUu6bn/nRVnV9VT1rx3B+sqtOq6rNV9cbpm/YADlgHLXoAAPbohUl+sbsflyRTDF/d3f+uqm6d5KNV9ZfTtg9Mcu/u/vx0/ye7+8qqOiTJJ6rqbd39wqp6dnffb43X+k+ZfaPhfZMcNj3mQ9O6Y5PcK8llST6a2beGfmTjdxdg/+BINMD+5VFJnlZV5yY5K8mdkhw9rfv4ioBOkudU1aeSfCzJkSu2250fSvKm7r6xu7+Y5K+S/LsVz72zu7+R5NzMTjMBOGA5Eg2wf6kkP9fdZ37LwqrjkvzzqvuPTPLg7v5qVX0wyW3W8dy787UVt2+Mf38ABzhHogGW21eS3H7F/TOTPKuqDk6Sqvr+qrrtGo+7Y5J/mgL6HkketGLd9Tc9fpUPJXnSdN711iQPS/LxDdkLgE3GkQSA5XZekhum0zJel+R3MjuV4pzpw327kjxhjcf9RZKfqarzklyU2SkdNzk1yXlVdU53/5cVy/8syYOTfCpJJ3lBd39hinAAVqjuXvQMAACwX3E6BwAADBLRAAAwSEQDAMAgEQ0AAINENAAADBLRAAAwSEQDAMAgEQ0AAIP+f9GErt9cdlCRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 2.375113\n",
      "(Epoch 0 / 20) train acc: 0.220000; val_acc: 0.091000\n",
      "(Epoch 1 / 20) train acc: 0.240000; val_acc: 0.096000\n",
      "(Epoch 2 / 20) train acc: 0.460000; val_acc: 0.152000\n",
      "(Epoch 3 / 20) train acc: 0.560000; val_acc: 0.121000\n",
      "(Epoch 4 / 20) train acc: 0.620000; val_acc: 0.165000\n",
      "(Epoch 5 / 20) train acc: 0.620000; val_acc: 0.158000\n",
      "(Iteration 11 / 40) loss: 1.094711\n",
      "(Epoch 6 / 20) train acc: 0.820000; val_acc: 0.173000\n",
      "(Epoch 7 / 20) train acc: 0.800000; val_acc: 0.171000\n",
      "(Epoch 8 / 20) train acc: 0.860000; val_acc: 0.172000\n",
      "(Epoch 9 / 20) train acc: 0.980000; val_acc: 0.198000\n",
      "(Epoch 10 / 20) train acc: 0.940000; val_acc: 0.183000\n",
      "(Iteration 21 / 40) loss: 0.278962\n",
      "(Epoch 11 / 20) train acc: 0.980000; val_acc: 0.177000\n",
      "(Epoch 12 / 20) train acc: 0.980000; val_acc: 0.196000\n",
      "(Epoch 13 / 20) train acc: 0.960000; val_acc: 0.169000\n",
      "(Epoch 14 / 20) train acc: 0.980000; val_acc: 0.177000\n",
      "(Epoch 15 / 20) train acc: 0.980000; val_acc: 0.187000\n",
      "(Iteration 31 / 40) loss: 0.044916\n",
      "(Epoch 16 / 20) train acc: 0.980000; val_acc: 0.186000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.180000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.184000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.178000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.177000\n",
      "==================================================\n",
      "weight_scale 1.000000e-02 learning_rate 1.000000e-02 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 188.174619\n",
      "(Epoch 0 / 20) train acc: 0.220000; val_acc: 0.119000\n",
      "(Epoch 1 / 20) train acc: 0.380000; val_acc: 0.150000\n",
      "(Epoch 2 / 20) train acc: 0.320000; val_acc: 0.156000\n",
      "(Epoch 3 / 20) train acc: 0.660000; val_acc: 0.153000\n",
      "(Epoch 4 / 20) train acc: 0.780000; val_acc: 0.172000\n",
      "(Epoch 5 / 20) train acc: 0.880000; val_acc: 0.174000\n",
      "(Iteration 11 / 40) loss: 5.796408\n",
      "(Epoch 6 / 20) train acc: 0.900000; val_acc: 0.176000\n",
      "(Epoch 7 / 20) train acc: 0.960000; val_acc: 0.179000\n",
      "(Epoch 8 / 20) train acc: 0.980000; val_acc: 0.179000\n",
      "(Epoch 9 / 20) train acc: 0.980000; val_acc: 0.179000\n",
      "(Epoch 10 / 20) train acc: 0.980000; val_acc: 0.179000\n",
      "(Iteration 21 / 40) loss: 0.000000\n",
      "(Epoch 11 / 20) train acc: 0.980000; val_acc: 0.179000\n",
      "(Epoch 12 / 20) train acc: 0.980000; val_acc: 0.172000\n",
      "(Epoch 13 / 20) train acc: 0.980000; val_acc: 0.172000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.170000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.170000\n",
      "(Iteration 31 / 40) loss: 0.000000\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.170000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.170000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.170000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.170000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.170000\n",
      "==================================================\n",
      "weight_scale 1.000000e-01 learning_rate 1.000000e-03 \n"
     ]
    },
    {
     "data": {
      "image/png": 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ADQAAIwjUAAAwgkANAAAjCNQAADCCQA0AACMI1AAAMIJADQAAI0wsUFfVyVX1/qq6rqquraoXDuPnV9Wuqrpq+Hr6vHXOq6rrq2pHVW2eVG0AALBcjpvgZ9+W5MXd/cmqum+SK6rqvcO8V3f3789fuKoenuTZSR6R5MQkf1tV39/d+ydYIwAAjDKxM9TdfWN3f3J4/9Uk1yXZuMgqZyV5S3d/s7s/n+T6JI+ZVH0AALAcVqSHuqpOSXJmko8NQ79aVVdX1euq6gHD2MYkN8xbbWcWD+AAADB1Ew/UVXWfJO9I8uvd/ZUkr0nyvUnOSHJjkj84sOgCq/cCn3dOVW2vqu179uyZUNUAALA0Ew3UVbU2c2H6jd19SZJ0903dvb+7b0/yZ/l2W8fOJCfPW/2kJLsP/szuvqi7N3X3pg0bNkyyfAAAOKxJ3uWjkrw2yXXd/ap54yfMW+xnk3x6eH9ZkmdX1T2r6tQkpyX5+KTqAwCA5TDJu3w8LskvJrmmqq4axl6W5DlVdUbm2jm+kOT5SdLd11bV25L8XebuEPICd/gAAGC1m1ig7u4PZ+G+6Hcvss7Lk7x8UjUBAMBy86REAAAYQaAGAIARBGoAABhBoAYAgBEEagAAGEGgBgCAEQRqAAAYQaAGAIARBGoAABhBoAYAgBEEagAAGEGgBgCAEQRqAAAYQaAGAIARjpt2AceSS6/clQu37cjuvfty4vp12br59Gw5c+O0ywIAYASBeoVceuWunHfJNdl36/4kya69+3LeJdckiVANADDDtHyskAu37bgjTB+w79b9uXDbjilVBADAchCoV8juvfuOaBwAgNkgUK+QE9evO6JxAABmg0C9QrZuPj3r1q6509i6tWuydfPpU6oIAIDl4KLEFXLgwkN3+QAAuHsRqFfQljM3CtAAAHczWj4AAGAEgRoAAEYQqAEAYASBGgAARjiiQF1z7j2pYgAAYNYcNlBX1V9W1f2q6l8luTbJ56vqRZMvDQAAVr+lnKF+ZHd/JcmWJH+T5KQkvzTJogAAYFYsJVDfo6qOS3JWkku7+1tJbp9sWQAAMBuWEqj/PMkXkzwgyQer6iFJvjbRqgAAYEYcNlB396u7+8Tufkp3d5Ibkjxx8qUBAMDqt5SLEn+1qu43vP/TJB9L8mOTLgwAAGbBUlo+zunur1TVU5JsTPLvkvzeZMsCAIDZsJRA3cPr05L8RXdfscT1AADgbm8pwfhTVfXuJD+d5D1VdZ98O2QDAMAx7bglLPPLSX4oyfXd/fWqOj7J2ZMtCwAAZsNhA3V37x9C9DOqKkk+2N3vmXhlAAAwA5Zyl4+XJ3lJks8NX1ur6ncmXRgAAMyCpbR8/HSSR3f3bUlSVa9L8skkvzHJwgAAYBYsJVAnyX2TfHnee44xl165Kxdu25Hde/flxPXrsnXz6dly5sZplwUAMHVLCdS/l+STVfW+JJXkCUl+c5JFsbpceuWunHfJNdl36/4kya69+3LeJdckiVANABzzlvLo8TckeXySdw9fP97db5x0YaweF27bcUeYPmDfrftz4bYdU6oIAGD1OOQZ6qr6wYOGrh9eH1RVD+ruqydXFqvJ7r37jmgcAOBYsljLx58sMq+T/Pgy18IqdeL6ddm1QHg+cf26KVQDALC6HDJQd/ePrWQhrF5bN59+px7qJFm3dk22bj59ilUBAKwOS73LB8ewAxceussHAMBdCdQsyZYzNwrQAAALOOxdPo5WVZ1cVe+vquuq6tqqeuEw/sCqem9VfWZ4fcAwXlX1R1V1fVVdXVWPnlRtAACwXA57hnqBu30kyS1Jbuju2xdZ9bYkL+7uT1bVfZNcUVXvTfJLSd7X3a+sqnOTnJvkpUmeluS04etHkrxmeAUAgFVrKS0fr01yRpJrM/dgl4cl+XSS+1fVOd39voVW6u4bk9w4vP9qVV2XZGOSszL3cJgkuTjJBzIXqM9K8pfd3Uk+WlXrq+qE4XMAAGBVWkrLx2eS/FB3n9Hdj0ryQ0muSrI5yR8sZSNVdUqSM5N8LMl3HgjJw+uDh8U2Jrlh3mo7h7GDP+ucqtpeVdv37NmzlM0DAMDELCVQP2z+Q1y6+5okj+7u6xdZ5w5VdZ8k70jy6939lcUWXWCs7zLQfVF3b+ruTRs2bFhKCQAAMDFLafn4bFX9cZK3DNPPSnJ9Vd0zc33Sh1RVazMXpt/Y3ZcMwzcdaOWoqhOS3DyM70xy8rzVT0qye4nfBwAATMVSzlA/N3Nh99wk52Uu5D4vc2H6SYdaqaoqc/3X13X3q+bNumxYP8PrO+eNP3e428djk9yifxoAgNXusGeou/vrSX53+DrYLYus+rgkv5jkmqq6ahh7WZJXJnlbVZ2d5ItJnjnMe3eSpye5PsnXk/zyUr4BAACYpqXcNu+xSX4ryXfPX767v3+x9br7w1m4LzpZ4Mz2cHePFxyuHgAAWE2W0kP9F0lekuSKJPsnWw4AAMyWpQTqr3T3X0+8EgAAmEFLCdSXV9UrklyS5JsHBuffSg8AAI5VSwnUjz/oNZm7P/SPL385AAAwW5Zyl48fW4lCAABgFh0yUFfVc7r7zVX17xea391/NLmyVq9Lr9yVC7ftyO69+3Li+nXZuvn0bDnzLk9IBwDgGLHYGeoHDK+e7z249MpdOe+Sa7Lv1rmbnezauy/nXXJNkgjVAADHqEMG6u7+z8Prf1y5cla3C7ftuCNMH7Dv1v25cNsOgRoA4Bi1lAe7HJ/k3yQ5JXd+sMs5kytrddq9d98Rja8mWlUAACZjKXf5eGeSjyb5cI7xB7ucuH5ddi0Qnk9cv24K1SydVhUAgMn5jiUsc+/ufnF3v6m733rga+KVrUJbN5+edWvX3Gls3do12br59ClVtDSLtaoAADDOUgL1e6rqKROvZAZsOXNjXvGMR2bj+nWpJBvXr8srnvHIVX+Wd5ZbVQAAVrultHz8SpKXVtXXk3wrSSXp7n7gRCtbpbacuXHVB+iDzWqrCgDALFjKGerjk6xNcv/M3ULv+LiV3kyZ1VYVAIBZsNiDXU7r7s8kecQhFrl6MiWx3A6cUXeXDwCA5bdYy8e5Sc5O8icLzOskPz6RipiIWWxVAQCYBYs92OXs4fXHVq4cAACYLUu5KDFV9dAkD09yrwNj3f2mSRUFAACzYilPSvyNJE9J8tAk25JsztxDXgRqAACOeUu5y8ezkvxEkhu7+xeTPCpLPLMNAAB3d0sJ1Pu6e3+S26rqvkn+Kcn3TLYsAACYDUs503xlVa1P8rok25N8JcknJ1oVAADMiEUDdVVVkvO7e2+SP6mqbUnu190CNQAA5DAtH93dSd41b/p6YRoAAL5tKT3UH6+qR0+8EgAAmEGLPXr8uO6+Lcnjk/yfVfXZJP+SpDJ38lrIBgDgmLdYD/XHkzw6yZYVqgUAAGbOYoG6kqS7P7tCtQAAwMxZLFBvqKoXHWpmd79qAvUAAMBMWSxQr0lynwxnqgEAgLtaLFDf2N2/vWKVAADADFrstnnOTAMAwGEsFqiftGJVAADAjDpkoO7uL61kIQAAMIuW8qREAADgEARqAAAYQaAGAIARBGoAABhBoAYAgBEEagAAGEGgBgCAEQRqAAAYQaAGAIARBGoAABhBoAYAgBEEagAAGEGgBgCAEQRqAAAYYWKBuqpeV1U3V9Wn542dX1W7quqq4evp8+adV1XXV9WOqto8qboAAGA5TfIM9euTPHWB8Vd39xnD17uTpKoenuTZSR4xrPOfq2rNBGsDAIBlMbFA3d0fSvKlJS5+VpK3dPc3u/vzSa5P8phJ1QYAAMtlGj3Uv1pVVw8tIQ8YxjYmuWHeMjuHsbuoqnOqantVbd+zZ8+kawUAgEWtdKB+TZLvTXJGkhuT/MEwXgss2wt9QHdf1N2bunvThg0bJlMlAAAs0YoG6u6+qbv3d/ftSf4s327r2Jnk5HmLnpRk90rWBgAAR2NFA3VVnTBv8meTHLgDyGVJnl1V96yqU5OcluTjK1kbAAAcjeMm9cFV9eYkT0hyfFXtTPJbSZ5QVWdkrp3jC0menyTdfW1VvS3J3yW5LckLunv/pGoDAIDlUt0LtirPhE2bNvX27dunXQYAAHdzVXVFd29aaJ4nJQIAwAgCNQAAjCBQAwDACAI1AACMIFADAMAIAjUAAIwgUAMAwAgCNQAAjCBQAwDACAI1AACMIFADAMAIAjUAAIwgUAMAwAgCNQAAjCBQAwDACAI1AACMIFADAMAIAjUAAIwgUAMAwAgCNQAAjCBQAwDACAI1AACMIFADAMAIAjUAAIwgUAMAwAgCNQAAjCBQAwDACAI1AACMIFADAMAIAjUAAIwgUAMAwAgCNQAAjCBQAwDACAI1AACMIFADAMAIAjUAAIwgUAMAwAgCNQAAjCBQAwDACAI1AACMIFADAMAIAjUAAIwgUAMAwAgCNQAAjCBQAwDACAI1AACMIFADAMAIAjUAAIwwsUBdVa+rqpur6tPzxh5YVe+tqs8Mrw8Yxquq/qiqrq+qq6vq0ZOqCwAAltMkz1C/PslTDxo7N8n7uvu0JO8bppPkaUlOG77OSfKaCdYFAADLZmKBurs/lORLBw2fleTi4f3FSbbMG//LnvPRJOur6oRJ1QYAAMtlpXuov7O7b0yS4fXBw/jGJDfMW27nMHYXVXVOVW2vqu179uyZaLEAAHA4q+WixFpgrBdasLsv6u5N3b1pw4YNEy4LAAAWt9KB+qYDrRzD683D+M4kJ89b7qQku1e4NgAAOGIrHagvS/K84f3zkrxz3vhzh7t9PDbJLQdaQwAAYDU7blIfXFVvTvKEJMdX1c4kv5XklUneVlVnJ/likmcOi787ydOTXJ/k60l+eVJ1AQDAcppYoO7u5xxi1pMWWLaTvGBStTB9l165Kxdu25Hde/flxPXrsnXz6dly5oLXnQIAzJSJBWo44NIrd+W8S67Jvlv3J0l27d2X8y65JkmEagBg5q2Wu3xwN3bhth13hOkD9t26Pxdu2zGligAAlo9AzcTt3rvviMYBAGaJQM3Enbh+3RGNAwDMEoGaidu6+fSsW7vmTmPr1q7J1s2nT6kiAIDl46JEJu7AhYfu8gEA3B0J1KyILWduFKABgLslLR8AADCCQA0AACMI1AAAMIJADQAAIwjUAAAwgkANAAAjCNQAADCCQA0AACMI1AAAMIJADQAAIwjUAAAwgkANAAAjCNQAADCCQA0AACMI1AAAMIJADQAAIwjUAAAwgkANAAAjCNQAADCCQA0AACMI1AAAMIJADQAAIwjUAAAwgkANAAAjCNQAADCCQA0AACMI1AAAMIJADQAAIwjUAAAwgkANAAAjCNQAADCCQA0AACMI1AAAMIJADQAAIwjUAAAwgkANAAAjCNQAADCCQA0AACMI1AAAMIJADQAAIxw3jY1W1ReSfDXJ/iS3dfemqnpgkrcmOSXJF5L8H9395WnUBwAASzXNM9Q/0d1ndPemYfrcJO/r7tOSvG+YBgCAVW01tXycleTi4f3FSbZMsRYAAFiSaQXqTvI3VXVFVZ0zjH1nd9+YJMPrg6dUGwAALNlUeqiTPK67d1fVg5O8t6r+fqkrDgH8nCR5yEMeMqn6AABgSaZyhrq7dw+vNyf5qySPSXJTVZ2QJMPrzYdY96Lu3tTdmzZs2LBSJQMAwIJWPFBX1b2r6r4H3id5SpJPJ7ksyfOGxZ6X5J0rXRsAABypabR8fGeSv6qqA9t/U3f/t6r6RJK3VdXZSb6Y5JlTqA0AAI7Iigfq7v5ckkctMP4/kzxppesBAIAxVtNt8wAAYOYI1AAAMIJADQAAIwjUAAAwgkANAAAjCNQAADCCQA0AACMI1AAAMIJADQAAIwjUAAAwgkANAAAjCNQAADCCQA0AACMI1AAAMIJADQAAIwjUAAAwgkANAAAjHDftAuBwLr1yVy7ctiO79+7LievXZevm07PlzI3TLgsAIIlAzSp36ZW7ct4l12TfrfuTJLv27st5l1yTJEI1ALAqaPlgVbtw2447wvQB+27dnwu37ZhSRQAAdyZQs6rt3rvviMYBAFaaQM2qduL6dUc0DgCw0gRqVrWtm0/PurVr7jS2bu2abN18+pQqAgC4MxclstUbcs8AAAxXSURBVKoduPDQXT4AgNVKoGbV23LmRgEaAFi1tHwAAMAIAjUAAIwgUAMAwAgCNQAAjCBQAwDACAI1AACMIFADAMAIAjUAAIwgUAMAwAgCNQAAjODR49ztXXrlrly4bUd2792XE9evy9bNp3uUOQCwbARq7tYuvXJXzrvkmuy7dX+SZNfefTnvkmuSZEVCtTAPAHd/Wj64W7tw2447wvQB+27dnwu37Zj4tg+E+V1796Xz7TB/6ZW7Jr5tAGDlCNTcre3eu++IxpfTNMM8ALByBGru1k5cv+6IxpfTNMM8ALByBGru1rZuPj3r1q6509i6tWuydfPpE9/2NMN8Mtdy8rhXXp5Tz/2vedwrL9dqAgATIlBzt7blzI15xTMemY3r16WSbFy/Lq94xiNX5MLAaYZ5/dsAsHLc5YO7vS1nbpzKnTUObHPMXT6O9i4hi/Vvu8sIACwvgRomaEyYH3PLP/3bALBytHzAKjXmLiHT7t8GgGOJM9SwSo05y7x18+l3OrudrFz/9gFjHmrjgTgAzBKBGg5jWuHuxPXrsmuB8LyUs8zL0b89xph2lWk/3ZLp8I8oYJYJ1LCIaYa7sWeZp3UxZjLuokgXVB57/CMKmHWrroe6qp5aVTuq6vqqOnfa9XBsm+bTDqd5y79k3H2sx7SruKDy2HMsP1XU/eLh7mFVnaGuqjVJ/iTJ/5ZkZ5JPVNVl3f13062MY9W0w920zjKPPWM4pl1lzLoHTLN/e5rrz+q2l+P3bBb323KcmZ/Vn/ksb1vts7ntSVtz/vnnT7uGO1xwwQWPTfKD3f3H559//v4LLrjgAUkeev755394oeUvuuii888555yVLZJjyv+7fWe++o3b7jK+cf26nP34U6dQ0co4++Lt+dLXv3Wnsdtu71yz65Ylfd8Puvc98sF/2JPbbu87xtatXZPf/OmH56En3G9i6ybfDikH6v/qN27LB/9hT056wLrDrj9m3WmvP8vbHvt7Nqv7bezv2Sz/zGd122qfzW0vlwsuuODG888//6KF5q22lo+NSW6YN71zGIOpmObTDqdp7BnDMe0qY1tdxrQPjG09mOb6s7ztsb9ns7rfxv6ezfLPfFa3PXZ9tU9n2ythVbV8JKkFxvpOC1Sdk+ScJHnIQx6yEjVxDJv23TKmZTnaLsa0q4xZd5r929Ncf5a3Pfb3bFb329jfs1n+mc/qtseur/bpbHslrLYz1DuTnDxv+qQku+cv0N0Xdfem7t60YcOGFS2OY9OWMzfmI+c+MZ9/5U/mI+c+8W4fppPZPjM/5qE2Yx+IM831Z3nbybjfs1ndb2N/z2b5Zz6r2x67vtqns+2VsNoC9SeSnFZVp1bVPZI8O8llU64JjjnTvsPIGGNCytiAM831Z3nbY83qfhv7ezbLP/NZ3fbY9dU+m3/GLMWquijx/PPPv/2CCy74TJI3Jvm1JG/o7nccankXJcLkPPSE++Xsx5+aX3/y9+fsx5+6ohd+jPHQE+6Xkx6wLtfsuiVf+8Zt2bh+XX7zpx++pJAyZt1prz/L2x5rVvfbgfWP9vdsln/ms7pttc/mtpfLYhclVncvND4TNm3a1Nu3b592GQAA3M1V1RXdvWmheaut5QMAAGaKQA0AACMI1AAAMIJADQAAIwjUAAAwgkANAAAjCNQAADCCQA0AACMI1AAAMIJADQAAIwjUAAAwgkANAAAjCNQAADCCQA0AACMI1AAAMIJADQAAIwjUAAAwQnX3tGs4alW1J8k/Tmnzxyf55ylte5bZb0fHfjs69tvRsd+Ojv12dOy3o2O/HZ0x++27u3vDQjNmOlBPU1Vt7+5N065j1thvR8d+Ozr229Gx346O/XZ07LejY78dnUntNy0fAAAwgkANAAAjCNRH76JpFzCj7LejY78dHfvt6NhvR8d+Ozr229Gx347ORPabHmoAABjBGWoAABhBoD5CVfXUqtpRVddX1bnTrmdWVNUXquqaqrqqqrZPu57VrKpeV1U3V9Wn5409sKreW1WfGV4fMM0aV6ND7Lfzq2rXcNxdVVVPn2aNq01VnVxV76+q66rq2qp64TDueFvEIvvN8baIqrpXVX28qj417LcLhvFTq+pjw/H21qq6x7RrXU0W2W+vr6rPzzvezph2ratRVa2pqiur6l3D9ESON4H6CFTVmiR/kuRpSR6e5DlV9fDpVjVTfqK7z3Cbn8N6fZKnHjR2bpL3dfdpSd43THNnr89d91uSvHo47s7o7nevcE2r3W1JXtzdD0vy2CQvGP5Mc7wt7lD7LXG8LeabSZ7Y3Y9KckaSp1bVY5P8bub222lJvpzk7CnWuBodar8lydZ5x9tV0ytxVXthkuvmTU/keBOoj8xjklzf3Z/r7m8leUuSs6ZcE3cz3f2hJF86aPisJBcP7y9OsmVFi5oBh9hvLKK7b+zuTw7vv5q5v3Q2xvG2qEX2G4voOV8bJtcOX53kiUnePow73g6yyH7jMKrqpCQ/meTPh+nKhI43gfrIbExyw7zpnfGH6FJ1kr+pqiuq6pxpFzODvrO7b0zm/jJP8uAp1zNLfrWqrh5aQrQuHEJVnZLkzCQfi+NtyQ7ab4njbVHDf79fleTmJO9N8tkke7v7tmERf68u4OD91t0HjreXD8fbq6vqnlMscbX6wyQvSXL7MP2gTOh4E6iPTC0w5l+JS/O47n505tplXlBVPz7tgjgmvCbJ92buv0lvTPIH0y1ndaqq+yR5R5Jf7+6vTLueWbHAfnO8HUZ37+/uM5KclLn/9X3YQoutbFWr38H7rap+IMl5SR6a5IeTPDDJS6dY4qpTVT+V5ObuvmL+8AKLLsvxJlAfmZ1JTp43fVKS3VOqZaZ09+7h9eYkf5W5P0hZupuq6oQkGV5vnnI9M6G7bxr+Iro9yZ/FcXcXVbU2c6Hwjd19yTDseDuMhfab423puntvkg9krgd9fVUdN8zy9+oi5u23pw6tR93d30zyF3G8HexxSX6mqr6QuRbdJ2bujPVEjjeB+sh8IslpwxWi90jy7CSXTbmmVa+q7l1V9z3wPslTknx68bU4yGVJnje8f16Sd06xlplxIBQOfjaOuzsZ+glfm+S67n7VvFmOt0Ucar853hZXVRuqav3wfl2SJ2eu//z9SX5uWMzxdpBD7Le/n/eP3spcH7DjbZ7uPq+7T+ruUzKX1y7v7p/PhI43D3Y5QsNtkP4wyZokr+vul0+5pFWvqr4nc2elk+S4JG+y3w6tqt6c5AlJjk9yU5LfSnJpkrcleUiSLyZ5Zne7AG+eQ+y3J2Tuv987yReSPP9AbzBJVT0+yX9Pck2+3WP4ssz1AzveDmGR/facON4Oqap+MHMXga3J3Am9t3X3bw9/R7wlc20LVyb5heGsK1l0v12eZEPm2hiuSvIr8y5eZJ6qekKS/6u7f2pSx5tADQAAI2j5AACAEQRqAAAYQaAGAIARBGoAABhBoAYAgBEEaoBVrqq+NryeUlX/epk/+2UHTf9/y/n5AMcCgRpgdpyS5IgCdVWtOcwidwrU3f2/HGFNAMc8gRpgdrwyyY9V1VVV9R+qak1VXVhVn6iqq6vq+cncQwyq6v1V9abMPXwkVXVpVV1RVddW1TnD2CuTrBs+743D2IGz4TV89qer6pqqeta8z/5AVb29qv6+qt44PKkN4Jh13OEXAWCVODfD076SZAjGt3T3D1fVPZN8pKr+Zlj2MUl+oLs/P0z/m+7+0vDo4k9U1Tu6+9yq+tXuPmOBbT0jc0/9e1Tmnj75iar60DDvzCSPSLI7yUeSPC7Jh5f/2wWYDc5QA8yupyR5blVdlbnHhT8oyWnDvI/PC9NJ8u+r6lNJPprk5HnLHcrjk7y5u/d3901JPpjkh+d99s7uvj1zjzw+ZVm+G4AZ5Qw1wOyqJL/W3dvuNFj1hCT/ctD0k5P8aHd/vao+kOReS/jsQ/nmvPf74+8S4BjnDDXA7PhqkvvOm96W5N9V1dokqarvr6p7L7De/ZN8eQjTD03y2Hnzbj2w/kE+lORZQ5/2hiQ/nuTjy/JdANzNOKsAMDuuTnLb0Lrx+iT/KXPtFp8cLgzck2TLAuv9tyS/UlVXJ9mRubaPAy5KcnVVfbK7f37e+F8l+dEkn0rSSV7S3f80BHIA5qnunnYNAAAws7R8AADACAI1AACMIFADAMAIAjUAAIwgUAMAwAgCNQAAjCBQAwDACAI1AACM8P8DO8NiFLK4j30AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 2.385904\n",
      "(Epoch 0 / 20) train acc: 0.100000; val_acc: 0.060000\n",
      "(Epoch 1 / 20) train acc: 0.100000; val_acc: 0.058000\n",
      "(Epoch 2 / 20) train acc: 0.140000; val_acc: 0.073000\n",
      "(Epoch 3 / 20) train acc: 0.220000; val_acc: 0.083000\n",
      "(Epoch 4 / 20) train acc: 0.280000; val_acc: 0.096000\n",
      "(Epoch 5 / 20) train acc: 0.400000; val_acc: 0.106000\n",
      "(Iteration 11 / 40) loss: 2.209372\n",
      "(Epoch 6 / 20) train acc: 0.460000; val_acc: 0.109000\n",
      "(Epoch 7 / 20) train acc: 0.420000; val_acc: 0.105000\n",
      "(Epoch 8 / 20) train acc: 0.500000; val_acc: 0.117000\n",
      "(Epoch 9 / 20) train acc: 0.520000; val_acc: 0.124000\n",
      "(Epoch 10 / 20) train acc: 0.540000; val_acc: 0.124000\n",
      "(Iteration 21 / 40) loss: 1.987063\n",
      "(Epoch 11 / 20) train acc: 0.540000; val_acc: 0.126000\n",
      "(Epoch 12 / 20) train acc: 0.560000; val_acc: 0.121000\n",
      "(Epoch 13 / 20) train acc: 0.580000; val_acc: 0.122000\n",
      "(Epoch 14 / 20) train acc: 0.600000; val_acc: 0.124000\n",
      "(Epoch 15 / 20) train acc: 0.580000; val_acc: 0.118000\n",
      "(Iteration 31 / 40) loss: 1.924150\n",
      "(Epoch 16 / 20) train acc: 0.600000; val_acc: 0.121000\n",
      "(Epoch 17 / 20) train acc: 0.620000; val_acc: 0.122000\n",
      "(Epoch 18 / 20) train acc: 0.600000; val_acc: 0.126000\n",
      "(Epoch 19 / 20) train acc: 0.560000; val_acc: 0.131000\n",
      "(Epoch 20 / 20) train acc: 0.580000; val_acc: 0.128000\n",
      "==================================================\n",
      "weight_scale 1.000000e-02 learning_rate 1.000000e-03 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 280.715356\n",
      "(Epoch 0 / 20) train acc: 0.140000; val_acc: 0.102000\n",
      "(Epoch 1 / 20) train acc: 0.160000; val_acc: 0.079000\n",
      "(Epoch 2 / 20) train acc: 0.100000; val_acc: 0.103000\n",
      "(Epoch 3 / 20) train acc: 0.100000; val_acc: 0.082000\n",
      "(Epoch 4 / 20) train acc: 0.120000; val_acc: 0.100000\n",
      "(Epoch 5 / 20) train acc: 0.080000; val_acc: 0.107000\n",
      "(Iteration 11 / 40) loss: 20065809002631822579742614635937792.000000\n",
      "(Epoch 6 / 20) train acc: 0.060000; val_acc: 0.079000\n",
      "(Epoch 7 / 20) train acc: 0.100000; val_acc: 0.103000\n",
      "(Epoch 8 / 20) train acc: 0.100000; val_acc: 0.102000\n",
      "(Epoch 9 / 20) train acc: 0.100000; val_acc: 0.102000\n",
      "(Epoch 10 / 20) train acc: 0.100000; val_acc: 0.102000\n",
      "(Iteration 21 / 40) loss: 2.300902\n",
      "(Epoch 11 / 20) train acc: 0.160000; val_acc: 0.112000\n",
      "(Epoch 12 / 20) train acc: 0.160000; val_acc: 0.112000\n",
      "(Epoch 13 / 20) train acc: 0.160000; val_acc: 0.112000\n",
      "(Epoch 14 / 20) train acc: 0.160000; val_acc: 0.112000\n",
      "(Epoch 15 / 20) train acc: 0.160000; val_acc: 0.112000\n",
      "(Iteration 31 / 40) loss: 2.299515\n",
      "(Epoch 16 / 20) train acc: 0.160000; val_acc: 0.112000\n",
      "(Epoch 17 / 20) train acc: 0.160000; val_acc: 0.112000\n",
      "(Epoch 18 / 20) train acc: 0.160000; val_acc: 0.112000\n",
      "(Epoch 19 / 20) train acc: 0.160000; val_acc: 0.112000\n",
      "(Epoch 20 / 20) train acc: 0.160000; val_acc: 0.112000\n",
      "==================================================\n",
      "weight_scale 1.000000e-01 learning_rate 8.000000e-03 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 2.305551\n",
      "(Epoch 0 / 20) train acc: 0.220000; val_acc: 0.123000\n",
      "(Epoch 1 / 20) train acc: 0.280000; val_acc: 0.118000\n",
      "(Epoch 2 / 20) train acc: 0.520000; val_acc: 0.168000\n",
      "(Epoch 3 / 20) train acc: 0.420000; val_acc: 0.161000\n",
      "(Epoch 4 / 20) train acc: 0.560000; val_acc: 0.173000\n",
      "(Epoch 5 / 20) train acc: 0.620000; val_acc: 0.160000\n",
      "(Iteration 11 / 40) loss: 1.238117\n",
      "(Epoch 6 / 20) train acc: 0.600000; val_acc: 0.184000\n",
      "(Epoch 7 / 20) train acc: 0.680000; val_acc: 0.183000\n",
      "(Epoch 8 / 20) train acc: 0.760000; val_acc: 0.185000\n",
      "(Epoch 9 / 20) train acc: 0.860000; val_acc: 0.202000\n",
      "(Epoch 10 / 20) train acc: 0.960000; val_acc: 0.189000\n",
      "(Iteration 21 / 40) loss: 0.627668\n",
      "(Epoch 11 / 20) train acc: 0.920000; val_acc: 0.186000\n",
      "(Epoch 12 / 20) train acc: 0.880000; val_acc: 0.166000\n",
      "(Epoch 13 / 20) train acc: 0.940000; val_acc: 0.167000\n",
      "(Epoch 14 / 20) train acc: 0.980000; val_acc: 0.185000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.185000\n",
      "(Iteration 31 / 40) loss: 0.109191\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.191000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.179000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.179000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.194000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.187000\n",
      "==================================================\n",
      "weight_scale 1.000000e-02 learning_rate 8.000000e-03 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a three-layer Net to overfit 50 training examples.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "weight_scales = [1e-1,1e-2]\n",
    "learning_rates = [1e-2,1e-3, 8e-3]\n",
    "for learning_rate in learning_rates:\n",
    "    for weight_scale in weight_scales:\n",
    "        model = FullyConnectedNet([100, 100],\n",
    "                      weight_scale=weight_scale, dtype=np.float64)\n",
    "        solver = Solver(model, small_data,\n",
    "                        print_every=10, num_epochs=20, batch_size=25,\n",
    "                        update_rule='sgd',\n",
    "                        optim_config={\n",
    "                          'learning_rate': learning_rate,\n",
    "                        }\n",
    "                 )\n",
    "        solver.train()\n",
    "        print(\"=\"*50)\n",
    "        print(\"weight_scale %e learning_rate %e \"%(weight_scale,learning_rate))\n",
    "        plt.plot(solver.loss_history, 'o')\n",
    "        plt.title('Training loss history')\n",
    "        plt.xlabel('Iteration')\n",
    "        plt.ylabel('Training loss')\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在尝试使用一个五层的网络，每层100个单位，以适应50个训练的例子。同样地，您将不得不调整学习速率和权重初始化，但是您应该能够在20个epoch内实现100%的训练精度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 77.617295\n",
      "(Epoch 0 / 20) train acc: 0.180000; val_acc: 0.122000\n",
      "(Epoch 1 / 20) train acc: 0.180000; val_acc: 0.115000\n",
      "(Epoch 2 / 20) train acc: 0.340000; val_acc: 0.091000\n",
      "(Epoch 3 / 20) train acc: 0.660000; val_acc: 0.121000\n",
      "(Epoch 4 / 20) train acc: 0.820000; val_acc: 0.117000\n",
      "(Epoch 5 / 20) train acc: 0.920000; val_acc: 0.116000\n",
      "(Iteration 11 / 40) loss: 0.000802\n",
      "(Epoch 6 / 20) train acc: 0.960000; val_acc: 0.119000\n",
      "(Epoch 7 / 20) train acc: 0.940000; val_acc: 0.114000\n",
      "(Epoch 8 / 20) train acc: 0.980000; val_acc: 0.113000\n",
      "(Epoch 9 / 20) train acc: 1.000000; val_acc: 0.115000\n",
      "(Epoch 10 / 20) train acc: 1.000000; val_acc: 0.118000\n",
      "(Iteration 21 / 40) loss: 0.000527\n",
      "(Epoch 11 / 20) train acc: 1.000000; val_acc: 0.118000\n",
      "(Epoch 12 / 20) train acc: 1.000000; val_acc: 0.118000\n",
      "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.118000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.118000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.118000\n",
      "(Iteration 31 / 40) loss: 0.000511\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.118000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.118000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.118000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.118000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.118000\n",
      "==================================================\n",
      "weight_scale 1.000000e-01 learning_rate 1.000000e-03 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 145.927326\n",
      "(Epoch 0 / 20) train acc: 0.180000; val_acc: 0.091000\n",
      "(Epoch 1 / 20) train acc: 0.200000; val_acc: 0.117000\n",
      "(Epoch 2 / 20) train acc: 0.160000; val_acc: 0.105000\n",
      "(Epoch 3 / 20) train acc: 0.480000; val_acc: 0.121000\n",
      "(Epoch 4 / 20) train acc: 0.580000; val_acc: 0.120000\n",
      "(Epoch 5 / 20) train acc: 0.780000; val_acc: 0.138000\n",
      "(Iteration 11 / 40) loss: 3.750227\n",
      "(Epoch 6 / 20) train acc: 0.820000; val_acc: 0.146000\n",
      "(Epoch 7 / 20) train acc: 0.860000; val_acc: 0.142000\n",
      "(Epoch 8 / 20) train acc: 0.880000; val_acc: 0.138000\n",
      "(Epoch 9 / 20) train acc: 0.940000; val_acc: 0.135000\n",
      "(Epoch 10 / 20) train acc: 0.960000; val_acc: 0.140000\n",
      "(Iteration 21 / 40) loss: 0.141620\n",
      "(Epoch 11 / 20) train acc: 0.980000; val_acc: 0.147000\n",
      "(Epoch 12 / 20) train acc: 0.960000; val_acc: 0.145000\n",
      "(Epoch 13 / 20) train acc: 0.980000; val_acc: 0.139000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.138000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.138000\n",
      "(Iteration 31 / 40) loss: 0.004810\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.139000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.138000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.138000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.138000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.138000\n",
      "==================================================\n",
      "weight_scale 1.000000e-01 learning_rate 2.000000e-03 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 107.525356\n",
      "(Epoch 0 / 20) train acc: 0.180000; val_acc: 0.086000\n",
      "(Epoch 1 / 20) train acc: 0.240000; val_acc: 0.115000\n",
      "(Epoch 2 / 20) train acc: 0.360000; val_acc: 0.137000\n",
      "(Epoch 3 / 20) train acc: 0.380000; val_acc: 0.103000\n",
      "(Epoch 4 / 20) train acc: 0.520000; val_acc: 0.124000\n",
      "(Epoch 5 / 20) train acc: 0.700000; val_acc: 0.118000\n",
      "(Iteration 11 / 40) loss: 12.933813\n",
      "(Epoch 6 / 20) train acc: 0.700000; val_acc: 0.111000\n",
      "(Epoch 7 / 20) train acc: 0.740000; val_acc: 0.123000\n",
      "(Epoch 8 / 20) train acc: 0.820000; val_acc: 0.118000\n",
      "(Epoch 9 / 20) train acc: 0.900000; val_acc: 0.113000\n",
      "(Epoch 10 / 20) train acc: 0.960000; val_acc: 0.111000\n",
      "(Iteration 21 / 40) loss: 0.000457\n",
      "(Epoch 11 / 20) train acc: 0.960000; val_acc: 0.111000\n",
      "(Epoch 12 / 20) train acc: 0.960000; val_acc: 0.116000\n",
      "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.114000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.115000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.111000\n",
      "(Iteration 31 / 40) loss: 0.000082\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.112000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.112000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.114000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.114000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.114000\n",
      "==================================================\n",
      "weight_scale 1.000000e-01 learning_rate 2.000000e-04 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a five-layer Net to overfit 50 training examples.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "weight_scales=[1e-1]\n",
    "learning_rates=[1e-3, 2e-3, 2e-4]\n",
    "for learning_rate in learning_rates:\n",
    "    for weight_scale in weight_scales:\n",
    "        model = FullyConnectedNet([100, 100, 100, 100],\n",
    "                        weight_scale=weight_scale, dtype=np.float64)\n",
    "        solver = Solver(model, small_data,\n",
    "                        print_every=10, num_epochs=20, batch_size=25,\n",
    "                        update_rule='sgd',\n",
    "                        optim_config={\n",
    "                          'learning_rate': learning_rate,\n",
    "                        }\n",
    "                 )\n",
    "        solver.train()\n",
    "        print(\"=\"*50)\n",
    "        print(\"weight_scale %e learning_rate %e \"%(weight_scale,learning_rate))\n",
    "        plt.plot(solver.loss_history, 'o')\n",
    "        plt.title('Training loss history')\n",
    "        plt.xlabel('Iteration')\n",
    "        plt.ylabel('Training loss')\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inline question: \n",
    "你注意到训练三层网和训练五层网的相对难度了吗?\n",
    "\n",
    "# Answer:  \n",
    "*fill in this*\n",
    "\n",
    "五层网络对参数初始化更加敏感，收敛和变化幅度相比三层网络大很多"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 更新规则\n",
    "到目前为止，我们已经使用了普通的随机梯度下降法(SGD)作为我们的更新规则。更复杂的更新规则可以更容易地训练深度网络。我们将实现一些最常用的更新规则，并将它们与普通的SGD进行比较。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SGD+Momentum\n",
    "具有动量的随机梯度下降法是一种广泛应用的更新规则，它使深部网络的收敛速度快于普通的随机梯度下降法。\n",
    "\n",
    "打开文件`cs231n/optim`，并阅读文件顶部的文档，以确保您理解了该API。在函数“sgd_momentum”中实现SSGD+Momentum更新规则，并运行以下命令检查实现。您应该看到错误小于1e-8。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  8.882347033505819e-09\n",
      "velocity error:  4.269287743278663e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.optim import sgd_momentum\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-3, 'velocity': v}\n",
    "next_w, _ = sgd_momentum(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [ 0.1406,      0.20738947,  0.27417895,  0.34096842,  0.40775789],\n",
    "  [ 0.47454737,  0.54133684,  0.60812632,  0.67491579,  0.74170526],\n",
    "  [ 0.80849474,  0.87528421,  0.94207368,  1.00886316,  1.07565263],\n",
    "  [ 1.14244211,  1.20923158,  1.27602105,  1.34281053,  1.4096    ]])\n",
    "expected_velocity = np.asarray([\n",
    "  [ 0.5406,      0.55475789,  0.56891579, 0.58307368,  0.59723158],\n",
    "  [ 0.61138947,  0.62554737,  0.63970526,  0.65386316,  0.66802105],\n",
    "  [ 0.68217895,  0.69633684,  0.71049474,  0.72465263,  0.73881053],\n",
    "  [ 0.75296842,  0.76712632,  0.78128421,  0.79544211,  0.8096    ]])\n",
    "\n",
    "print('next_w error: ', rel_error(next_w, expected_next_w))\n",
    "print('velocity error: ', rel_error(expected_velocity, config['velocity']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一旦你这样做了，运行以下来训练一个具有SGD和SGD+Momentum的六层网络。你应该看到SGD+Momentum更新规则收敛得更快。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  sgd\n",
      "(Iteration 1 / 200) loss: 2.482885\n",
      "(Epoch 0 / 5) train acc: 0.115000; val_acc: 0.134000\n",
      "(Iteration 11 / 200) loss: 2.212039\n",
      "(Iteration 21 / 200) loss: 2.119283\n",
      "(Iteration 31 / 200) loss: 2.088308\n",
      "(Epoch 1 / 5) train acc: 0.279000; val_acc: 0.247000\n",
      "(Iteration 41 / 200) loss: 2.009054\n",
      "(Iteration 51 / 200) loss: 1.953987\n",
      "(Iteration 61 / 200) loss: 2.069506\n",
      "(Iteration 71 / 200) loss: 1.891622\n",
      "(Epoch 2 / 5) train acc: 0.333000; val_acc: 0.271000\n",
      "(Iteration 81 / 200) loss: 1.958631\n",
      "(Iteration 91 / 200) loss: 1.945542\n",
      "(Iteration 101 / 200) loss: 1.762355\n",
      "(Iteration 111 / 200) loss: 1.866230\n",
      "(Epoch 3 / 5) train acc: 0.319000; val_acc: 0.306000\n",
      "(Iteration 121 / 200) loss: 1.755194\n",
      "(Iteration 131 / 200) loss: 1.825922\n",
      "(Iteration 141 / 200) loss: 1.723891\n",
      "(Iteration 151 / 200) loss: 1.689054\n",
      "(Epoch 4 / 5) train acc: 0.399000; val_acc: 0.314000\n",
      "(Iteration 161 / 200) loss: 1.682975\n",
      "(Iteration 171 / 200) loss: 1.781320\n",
      "(Iteration 181 / 200) loss: 1.593739\n",
      "(Iteration 191 / 200) loss: 1.598864\n",
      "(Epoch 5 / 5) train acc: 0.396000; val_acc: 0.328000\n",
      "\n",
      "running with  sgd_momentum\n",
      "(Iteration 1 / 200) loss: 2.604007\n",
      "(Epoch 0 / 5) train acc: 0.100000; val_acc: 0.077000\n",
      "(Iteration 11 / 200) loss: 2.231150\n",
      "(Iteration 21 / 200) loss: 2.080558\n",
      "(Iteration 31 / 200) loss: 1.800901\n",
      "(Epoch 1 / 5) train acc: 0.311000; val_acc: 0.304000\n",
      "(Iteration 41 / 200) loss: 2.052403\n",
      "(Iteration 51 / 200) loss: 1.853793\n",
      "(Iteration 61 / 200) loss: 1.762008\n",
      "(Iteration 71 / 200) loss: 1.759815\n",
      "(Epoch 2 / 5) train acc: 0.376000; val_acc: 0.327000\n",
      "(Iteration 81 / 200) loss: 1.810102\n",
      "(Iteration 91 / 200) loss: 1.692159\n",
      "(Iteration 101 / 200) loss: 1.651033\n",
      "(Iteration 111 / 200) loss: 1.515031\n",
      "(Epoch 3 / 5) train acc: 0.428000; val_acc: 0.341000\n",
      "(Iteration 121 / 200) loss: 1.436063\n",
      "(Iteration 131 / 200) loss: 1.431370\n",
      "(Iteration 141 / 200) loss: 1.468514\n",
      "(Iteration 151 / 200) loss: 1.506089\n",
      "(Epoch 4 / 5) train acc: 0.470000; val_acc: 0.342000\n",
      "(Iteration 161 / 200) loss: 1.330009\n",
      "(Iteration 171 / 200) loss: 1.543547\n",
      "(Iteration 181 / 200) loss: 1.508076\n",
      "(Iteration 191 / 200) loss: 1.363205\n",
      "(Epoch 5 / 5) train acc: 0.494000; val_acc: 0.365000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:39: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "D:\\Python\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:42: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "D:\\Python\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:45: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "D:\\Python\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:39: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "D:\\Python\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:42: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "D:\\Python\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:45: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "D:\\Python\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:49: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_train = 4000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "solvers = {}\n",
    "\n",
    "for update_rule in ['sgd', 'sgd_momentum']:\n",
    "  print('running with ', update_rule)\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-2,\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in list(solvers.items()):\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RMSProp and Adam\n",
    "RMSProp [1] 和Adam [2] ，它们通过使用梯度的第二个时刻的运行平均值来设置每个参数的学习速率。\n",
    "在文件`cs231n/optim`中在`RMSProp`函数中实现RMSProp更新规则，在`Adam`函数中实现Adam更新规则，并使用下面的测试来检查您的实现。\n",
    "\n",
    "[1] Tijmen Tieleman and Geoffrey Hinton. \"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.\" COURSERA: Neural Networks for Machine Learning 4 (2012).\n",
    "\n",
    "[2] Diederik Kingma and Jimmy Ba, \"Adam: A Method for Stochastic Optimization\", ICLR 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  9.524687511038133e-08\n",
      "cache error:  2.6477955807156126e-09\n"
     ]
    }
   ],
   "source": [
    "# Test RMSProp implementation; you should see errors less than 1e-7\n",
    "from cs231n.optim import rmsprop\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "cache = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'cache': cache}\n",
    "next_w, _ = rmsprop(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.39223849, -0.34037513, -0.28849239, -0.23659121, -0.18467247],\n",
    "  [-0.132737,   -0.08078555, -0.02881884,  0.02316247,  0.07515774],\n",
    "  [ 0.12716641,  0.17918792,  0.23122175,  0.28326742,  0.33532447],\n",
    "  [ 0.38739248,  0.43947102,  0.49155973,  0.54365823,  0.59576619]])\n",
    "expected_cache = np.asarray([\n",
    "  [ 0.5976,      0.6126277,   0.6277108,   0.64284931,  0.65804321],\n",
    "  [ 0.67329252,  0.68859723,  0.70395734,  0.71937285,  0.73484377],\n",
    "  [ 0.75037008,  0.7659518,   0.78158892,  0.79728144,  0.81302936],\n",
    "  [ 0.82883269,  0.84469141,  0.86060554,  0.87657507,  0.8926    ]])\n",
    "\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('cache error: ', rel_error(expected_cache, config['cache']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  0.20720703668629928\n",
      "v error:  4.208314038113071e-09\n",
      "m error:  4.214963193114416e-09\n"
     ]
    }
   ],
   "source": [
    "# Test Adam implementation; you should see errors around 1e-7 or less\n",
    "from cs231n.optim import adam\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "m = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.7, 0.5, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'm': m, 'v': v, 't': 5}\n",
    "next_w, _ = adam(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.40094747, -0.34836187, -0.29577703, -0.24319299, -0.19060977],\n",
    "  [-0.1380274,  -0.08544591, -0.03286534,  0.01971428,  0.0722929],\n",
    "  [ 0.1248705,   0.17744702,  0.23002243,  0.28259667,  0.33516969],\n",
    "  [ 0.38774145,  0.44031188,  0.49288093,  0.54544852,  0.59801459]])\n",
    "expected_v = np.asarray([\n",
    "  [ 0.69966,     0.68908382,  0.67851319,  0.66794809,  0.65738853,],\n",
    "  [ 0.64683452,  0.63628604,  0.6257431,   0.61520571,  0.60467385,],\n",
    "  [ 0.59414753,  0.58362676,  0.57311152,  0.56260183,  0.55209767,],\n",
    "  [ 0.54159906,  0.53110598,  0.52061845,  0.51013645,  0.49966,   ]])\n",
    "expected_m = np.asarray([\n",
    "  [ 0.48,        0.49947368,  0.51894737,  0.53842105,  0.55789474],\n",
    "  [ 0.57736842,  0.59684211,  0.61631579,  0.63578947,  0.65526316],\n",
    "  [ 0.67473684,  0.69421053,  0.71368421,  0.73315789,  0.75263158],\n",
    "  [ 0.77210526,  0.79157895,  0.81105263,  0.83052632,  0.85      ]])\n",
    "\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('v error: ', rel_error(expected_v, config['v']))\n",
    "print('m error: ', rel_error(expected_m, config['m']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一旦你调试了你的RMSProp和Adam实现，运行下面的代码来使用这些新的更新规则来训练一对深层网络:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  adam\n",
      "(Iteration 1 / 200) loss: 3.198212\n",
      "(Epoch 0 / 5) train acc: 0.131000; val_acc: 0.144000\n",
      "(Iteration 11 / 200) loss: 2.276926\n",
      "(Iteration 21 / 200) loss: 2.088783\n",
      "(Iteration 31 / 200) loss: 2.089586\n",
      "(Epoch 1 / 5) train acc: 0.278000; val_acc: 0.272000\n",
      "(Iteration 41 / 200) loss: 2.031683\n",
      "(Iteration 51 / 200) loss: 1.809372\n",
      "(Iteration 61 / 200) loss: 1.890397\n",
      "(Iteration 71 / 200) loss: 1.920178\n",
      "(Epoch 2 / 5) train acc: 0.333000; val_acc: 0.298000\n",
      "(Iteration 81 / 200) loss: 1.631650\n",
      "(Iteration 91 / 200) loss: 1.674013\n",
      "(Iteration 101 / 200) loss: 1.748246\n",
      "(Iteration 111 / 200) loss: 1.708306\n",
      "(Epoch 3 / 5) train acc: 0.411000; val_acc: 0.350000\n",
      "(Iteration 121 / 200) loss: 1.864546\n",
      "(Iteration 131 / 200) loss: 1.692495\n",
      "(Iteration 141 / 200) loss: 1.580489\n",
      "(Iteration 151 / 200) loss: 1.515855\n",
      "(Epoch 4 / 5) train acc: 0.408000; val_acc: 0.352000\n",
      "(Iteration 161 / 200) loss: 1.622686\n",
      "(Iteration 171 / 200) loss: 1.497883\n",
      "(Iteration 181 / 200) loss: 1.593961\n",
      "(Iteration 191 / 200) loss: 1.512819\n",
      "(Epoch 5 / 5) train acc: 0.442000; val_acc: 0.375000\n",
      "\n",
      "running with  rmsprop\n",
      "(Iteration 1 / 200) loss: 2.953507\n",
      "(Epoch 0 / 5) train acc: 0.174000; val_acc: 0.143000\n",
      "(Iteration 11 / 200) loss: 1.941673\n",
      "(Iteration 21 / 200) loss: 1.970141\n",
      "(Iteration 31 / 200) loss: 1.828054\n",
      "(Epoch 1 / 5) train acc: 0.381000; val_acc: 0.308000\n",
      "(Iteration 41 / 200) loss: 1.846869\n",
      "(Iteration 51 / 200) loss: 1.869567\n",
      "(Iteration 61 / 200) loss: 1.822185\n",
      "(Iteration 71 / 200) loss: 1.766039\n",
      "(Epoch 2 / 5) train acc: 0.395000; val_acc: 0.337000\n",
      "(Iteration 81 / 200) loss: 1.674907\n",
      "(Iteration 91 / 200) loss: 1.651398\n",
      "(Iteration 101 / 200) loss: 1.479031\n",
      "(Iteration 111 / 200) loss: 1.383396\n",
      "(Epoch 3 / 5) train acc: 0.478000; val_acc: 0.366000\n",
      "(Iteration 121 / 200) loss: 1.503549\n",
      "(Iteration 131 / 200) loss: 1.485049\n",
      "(Iteration 141 / 200) loss: 1.579919\n",
      "(Iteration 151 / 200) loss: 1.366413\n",
      "(Epoch 4 / 5) train acc: 0.535000; val_acc: 0.362000\n",
      "(Iteration 161 / 200) loss: 1.438344\n",
      "(Iteration 171 / 200) loss: 1.543190\n",
      "(Iteration 181 / 200) loss: 1.449654\n",
      "(Iteration 191 / 200) loss: 1.401302\n",
      "(Epoch 5 / 5) train acc: 0.548000; val_acc: 0.376000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:30: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "D:\\Python\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:33: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "D:\\Python\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:36: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "D:\\Python\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:30: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "D:\\Python\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:33: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "D:\\Python\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:36: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "D:\\Python\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:30: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "D:\\Python\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:33: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "D:\\Python\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:36: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "D:\\Python\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:30: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "D:\\Python\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:33: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "D:\\Python\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:36: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "D:\\Python\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:40: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rates = {'rmsprop': 1e-4, 'adam': 1e-3}\n",
    "for update_rule in ['adam', 'rmsprop']:\n",
    "  print('running with ', update_rule)\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': learning_rates[update_rule]\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in list(solvers.items()):\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 训练一个好的模型!\n",
    "在CIFAR-10上尽可能训练最好的全连接模型，将最好的模型存储在`best_model`变量中。我们要求您在使用全网连接的验证集上获得至少50%的准确性。\n",
    "\n",
    "如果你小心，它应该是有可能得到精度55%以上，但我们不要求它为这部分，也不会分配额外的学分这样做。在作业的后面，我们会要求你们在CIFAR-10上尽可能地训练最好的卷积网络，我们希望你们把精力放在卷积网络上，而不是全连接网络上。\n",
    "\n",
    "您可能会发现完成`BatchNormalization.ipynb` and `Dropout.ipynb` 非常有用。在完成这部分之前，我先温习一下笔记本，因为这些技术可以帮助你训练强大的模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 2450) loss: 2.339706\n",
      "(Epoch 0 / 5) train acc: 0.106000; val_acc: 0.111000\n",
      "(Epoch 1 / 5) train acc: 0.513000; val_acc: 0.460000\n",
      "(Iteration 501 / 2450) loss: 1.641750\n",
      "(Epoch 2 / 5) train acc: 0.559000; val_acc: 0.503000\n",
      "(Iteration 1001 / 2450) loss: 1.311759\n",
      "(Epoch 3 / 5) train acc: 0.600000; val_acc: 0.506000\n",
      "(Iteration 1501 / 2450) loss: 1.153394\n",
      "(Epoch 4 / 5) train acc: 0.659000; val_acc: 0.530000\n",
      "(Iteration 2001 / 2450) loss: 1.058192\n",
      "(Epoch 5 / 5) train acc: 0.697000; val_acc: 0.528000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:34: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "D:\\Python\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:37: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "D:\\Python\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:40: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "D:\\Python\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:44: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "data": {
      "image/png": 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SbgvhuQEAAADgpBGyoGatXS6pc6jOFw4U1AAAAABEgpBteP1nQOsjAAAAgEhAUAMAAACACENQc2E8PwAAAIBIQFALghZIAAAAAOFEUHMhoAEAAACIBAS1IIwJ9xUAAAAAOJkR1IKgsgYAAAAgnAhqLpaEBgAAACACENQAAAAAIMIQ1FwoqAEAAACIBAQ1AAAAAIgwBDUXCmoAAAAAIgFBzYXWRwAAAACRgKAGAAAAABGGoOZifc2PVNYAAAAAhBNBDQAAAAAiDEHNxV9Jo6AGAAAAIJwIai7+gGbpfQQAAAAQRgQ1AAAAAIgwBDU3XyXNQ0UNAAAAQBgR1IIgpwEAAAAIJ4Kaiy3yfwAAAAAIB4JaELQ+AgAAAAgngpqLpaQGAAAAIAIQ1FysL6GR0wAAAACEE0EtCFofAQAAAIQTQc3Fn8/IaQAAAADCiaAWhKX5EQAAAEAYEdRc/PHM4wnrZQAAAAA4yRHUAAAAACDCENRcCteo0foIAAAAIHwIai7+tWkechoAAACAMCKoBcEwEQAAAADhRFBzYzw/AAAAgAhAUAuCnAYAAAAgnAhqLv6AxjARAAAAAOEU0qBmjIk2xiwzxvwcyvOeKP6ARk4DAAAAEE6hrqg9LGldiM95wpHTAAAAAIRTyIKaMSZe0sWSRobqnCeav5LmoaQGAAAAIIxCWVF7U9KTkjwlHWCMudsYs9gYszglJSWETx1a5DQAAAAA4RSSoGaMuUTSPmvtktKOs9aOsNZ2ttZ2rl27diieOqQYJgIAAAAgEoSqotZT0mXGmG2SvpR0jjFmdIjOfcKR0wAAAACEU0iCmrV2kLU23lqbIOl6SdOstTeF4twnkj+gkdMAAAAAhBP7qLlY+cfzE9UAAAAAhE9MqE9orZ0haUaoz3siechpAAAAAMKIippLYesjSQ0AAABA+BDUgqDzEQAAAEA4EdSCIKgBAAAACCeCmot/iAitjwAAAADCiaAWBBU1AAAAAOFEUHPx5zMPSQ0AAABAGBHUgiCmAQAAAAgngpqLM56fpAYAAAAgjAhqJbCkNQAAAABhQlBzcU97JKcBAAAACBeCmos7nJHTAAAAAIQLQa0ETH4EAAAAEC4ENRd3NCOnAQAAAAgXgloJLM2PAAAAAMKEoOYSsEaNnAYAAAAgTAhqAZj6CAAAACD8CGoloPURAAAAQLgQ1FxofQQAAAAQCQhqJWA8PwAAAIBwIai5sOE1AAAAgEhAUCsBBTUAAAAA4UJQc7EBUx9JagAAAADCg6DmwjARAAAAAJGAoFYCchoAAACAcCGoubjDGVMfAQAAAIQLQa0E5DQAAAAA4UJQcwkcz09SAwAAABAeBDWXwKmPYbwQAAAAACc1gloJCGoAAAAAwoWg5uYKZ4dz8sN3HQAAAABOagS1Epz3xsxwXwIAAACAkxRBzeWaTvEadGHrcF8GAAAAgJMcQc3lrOa1dE+fZurftq4kKTuvIMxXBAAAAOBkRFAL4ppO8ZKk1bvSlF/g0c4DmWG+IgAAAAAnE4JaEO0bVpUkfTR3q65+b556DZ+uHalHD2vvzdishIHjlZvvOd6XCAAAAOBPLCRBzRjTyBgz3RizzhizxhjzcCjOGy51q8RJkias2qMVSWmSpGU7D8rjKX1m/wezNkuSUo/kHN8LBAAAAPCnFqqKWr6kx621bSR1l3S/MaZtiM59wkVHmWKPPfzlcjV9aoISBo7X6AXblTBwvPamZwccExcTLUnan5F7Qq5TktKy8lhLBwAAAPzJhCSoWWuTrbVLfX/OkLROUsNQnDtcfn7wbD18bgtdcUYDPXFBq4D3PfPDaknSwq0HNGr+Ni3YkipJiov1fjn3ZXgD3M4DmfrP9E2yx3H37A7PTda1H8w/bucHAAAAcOLFhPqExpgESR0lLQzyvrsl3S1JjRs3DvVTh9RpDavqNN9atfV70vXqpA3Fjtm2/4je+HWjJG+w2+Zbx3YwM0+SdPeoJVqXnK4rOjZUw2oVjtu1rvS1ZwIAAAD4cwjpMBFjTCVJ30l6xFqbXvT91toR1trO1trOtWvXDuVTH1et6lYO+rg/pEnSFf+Z6/w5I9sb1FIyvGvVDmfnO+9bl5yup79fpb9/s0IHj+Q6xx3KPHHtkgAAAAAiW8iCmjEmVt6QNsZaOzZU540Exhh9cluXUo/Jdw0a2XUwS+NXJisn37t2bHZiihIGjteva/fq8a9XaMzCHfp2SZJem7xBny/coS4vTtEZw34tds7cfI8T+oIpKDLcJL/AE9BmmVfg0Ydztir18LEPN7n6vXl6+vtVx/xxkS47ryBgTd/hnHx1HDZZsxNTwnhVAAAAQKBQTX00kj6UtM5a+0Yozhlp+raqo5E3d3bevqd30xKPHTlnq+7/fKkyfJW0F8avkyTd9dlirU0uLDSOWbhDT7nCUF6BR0dyCqtvt368SO2HTnbettZqZdIhJ4y5A8c3i3eq+dO/6NN527Ri5yHtS8/Wl4t26Pmf12rMwh1Br3PpjoNal5yu+8YsUeLejID3Ldl+UGMW7tDkNXvU/42ZSsvM04Y9GUedfHmsDh7J1dilSSE9Z2lOGzJJnZ4vDMUb9mToYGaeXp+8sZSPAgAAAE6sUK1R6ynpr5JWGWOW+x57ylo7IUTnjwjnta2rSY/01oqkQ7rmzHi1qldZMzemaNzy3SE5/8DvVum7pUkad39PdWhUTfM2e4eUPPHNCtWsVF5dm1TX7Z8s1jMXt9GdvZoqyxXUnvh2pSRp6E9rncfu6eMNk8lphdMpc/ILtC89R/HVK+iqd+c5j09YtUfbXrm42DXdPWqJJOmtaYn6cM5WPX1RG93lCqkej1XrwRP1SP8Wuq9v82P+nB/7ermmb0hRx8bV1aTWKcf88ZI34MZEGXnvF0hpmXkqHxuluNjoYsfme6zycwu/bh5f6A026RMAAAAIl1BNfZxjrTXW2tOttWf4/vtThTS/VvUq69rOjRQVZXTVmfG6rWeTkJ37O19l6fL/zNWPKwrD3zdLkvT+zM2anbhfkjRzY4qy8wqUlVv6WP4PZm6RJG1PPeI8NviH1eo1fLr2FNlawO/1yRu0bMfBYo9v9FXclu08qPdmbNY6X2VwbXK6cgs8Gj7RO2wlLStP93++NGi7ZWZuvtoPmaRf1+51Htub7j0uIztPOfkFAa2eZZ2W2eLpXwIqkx2GTdZNI4vNsgnKXyE8lpxW4LG6b8wSLdl+oMRjflqxWwkDx2vXoayg79+ScljWWuUVsDk6AAAAigvpMJGTUYf4qvpLp3jd3ONU/fpob118en1J0jmt60iSrjijgX59tLcGXdj6mM770BfLij328dxtkqTZifvVevBE3frxojKda97mVO06lKVBY1fq68XeMLh535Fix+06lKW3p23Sla5Km59/MMra3en658T1uvydudq2/4izFUFstNHCLam6d9QSjV+ZrL6vzijWTrkl5YgycvJ112eLNXnNHklSed+WBgeO5Oq6DxY4rZ7/mb5JTQZNUP5Rgox/IMsXi3YGPL54+8GjDmhJy8xzqo1RJnhS83/ebnvSszVh1R5d98ECzdiwr9haQUn6YdkuSdKaXcUncs7amKJzXp+pc1+fqdaDJ2r/71hD+Geybf+REgMtAADAyYqg9j8yxujVv3TQsMtPU4u6ldXU177Xom4ljb3vLL10VXu1qFtZ9/Rppm2vXKw5/+gXsufenFI8bJWk5yvTAsLMTR8Wrzj1fGVaiR+/fo83dPm3IMgt8KjvazP0jS/45RVY3fLxIs337SmXkZOvG0cu1P+NXKDHv14hSZrva+WUvC2VHo9V+Rjvt+DqXWlavvOQJG8l7T/TN0mSFm09oDmJ+zVo7KqA0LZhT4ay8wrU0bXebOv+I7rz09+ct//PV1VLz87Tku0HAyp0Ow9kquc/p+mRr7ydusGC2vT1+9TlxSnFBo34w2G+x+rWj3/T+zM3q8BTWB0r8Fgt2uqttg0au0rjlu9SwsDx6vPqdEnetYGStGX/ERV4rPakBa9u+n23JEnJaYVBJjM3X2mZJQ+ZCaWM7LygYVU6esUzN9+jR79ars0phwMeL/BYfTJ3q7PGsu9rM0r93jua3HyPMnPzj37gcXDfmCUaMWtzWJ4bAAD8uRHUQqxctPdLamR0ZuPqqlgucBlgfPWKurFb4R5y/7nxzGM6f+W4Y1tWeFmHBsd0/LH6ZfUe58/ZeYHVr5SMHM3dlKrvliZpzMLtenHCuoD3N31qgnYe8AaQ11zDPO4etUSNa1SUJN04cqFu+nChvli0Q1v2e4PpwSO5uuDNWU4Q87vxvws0Zd0+5+01u9OVuDdDpw+drKvfmxcQVHsNn67DrsEtxnjX732/LEnWWk1es8cJVH/9cJHuG7PEaZNMPRJYqUvcm6Gr3pun9kMnKfVwjn5euVsZvnOnHsnVw196w+D21Ewdysx1hsz4udcauq3YeUgJA8fr8W9W6JaPvNVTa63aPjtJHYZNlsdjNXZpkjNd1G1HaqYyc/Pl8diAoTMLt6Rq8TZviFy7O90Joev3pOvLRcWHzpz/r1nq8uKUYo9/Om+bmgyaEFAN3JOWrYSB4zXF19q6fOchfb9slx75crk8HqvVvuriuOW7NPSntU4Y90sYON455lhc+8F8tX12kiRvK+vE1d6Jq8lpWUo66L2xcLQ24d9rwqo9emnC+uNy7mDSsvK080DmCXs+AAAQPgS1EIvyLXYqrdrw0pXtNeuJflox5Hy1a1DlmM6/augFQR8vaQrluW3q6LH+LY/pOY6Hp79fHfTxYC1vv67d61Tw3LanZiph4HinirZke+Baupz84m2SA8cWrl2bum5vsff7zducqlbPTNSjX61Qk0ETdPeoJXp7WmGQmLBqjxb6qmT7iqzvyyuwWrHzkLLzPOr0whQlHSy5jW9dcoZ2FXn/oUzvi+99GdnKyvWu01u4JVU/ryxcp7hx72H1fXW6Brw523lszMLteuzrFfrE1xLrV+Cx6v3qdD3w+TL9c9J6tR480an2XTdiga55f74k6aK3ZuuvH3oD4IA3Zwd8rfz8raHfLknS0B/XyFqr9Ow8vTvD+7Xp/MIUjV2apIVbUvW5L+h9+Zv3/7+sTpYkrdqVplELtuuSt+dodmKK0rPynM+7qHHLd5X4tSuJvxIrScuTDune0Us18LtV6vHyNJ39z+mak7hfbZ6dGLCmMFi4dRu7NCkgyAezYEtqqe8vSVpmno7k5Cu/wKPTh07Sd0tKnnq680CmrLX658T12nkgUxf9e7Z6DZ9e7Dhrrd6dsYkWUnnX05Z1fetLE9bp79+sOM5XBADA70NQC7HLOjRQlbgY/aVzo1KPa1yzoqpWiFVCrVM06ZHeGnxJW71wxWnH/HzlY6IUG2006KI2Qd9fpUKsHjq3haY93ifo+//PVd0rzeaXLjrmawu1uz5bHPTxi9rXkxS8auIOc1PX7yv2/mNxw38X6JK3Z+u3bYFDRIq+oH9/ZmArnHtQyQ3/XaCJa/YEvP+Bz5eq1/Dp6vriVN32ySK9O2OzrhuxwGmf9NuWmqkNrnV/n/sqhO49/HYeyNReX5Cctn6fM1CmaHvlYtfnMMgV0L5YtMN5ketuNf37Nyv0ia+KdvrQyc4QGEl67OsVum7EAr01NVGSt1InFa6plKQ1u72VspVJhRUzY6SvFweuLfxpRbJGL9juvP3Grxs1ec0efbN4Z8CL79TDOUoYOD5g6E5egUe3fextff1+WWHg8z/H1e/N16gF23XfmCVq9cxEDf5htaau26sCj9W/pyTqgK9SumLnIT329Qo9c5R9BK8fsSDo40u2H1TCwPEaOXuLJhX5u5a8w276vDpdGdn5Ss/O19Cf1kjyVgMTBo7XG796q8u/rEpWr+HTNWrBdr03Y7PuHrWkxCC2OeWwhk/coIeDrG09mSzZflDn/2uWPp23TYdz8pUwcLx+WZWs9Ow85Qa5kTNi1hZ9W0pQBgAgnAhqIdaoRkWtHHqBmtepVOaPaVWvsu44u4lu6n6qNrwwwHn83j7NnD9XqxjrVN96tajlPP7K1e2V+GLwEHV281rq1qSGJKlp7cLreegc7xj9oZe21bDLT9NtPRP0l07xkuSsGTBeKtUAACAASURBVHO7p09TRUcZVa0QW+bPqTT39W2mfwxorWa1Sx/H//ODZ+vT27tq8TPnlXrc3/o0V6MaFUpsIQyl1bvSnYEsfjM3Bq5hK9ra6LFSy7olfz+4K4ELthxw2v9WJJXeBuifvDlzQ4r+NnqJRi/Yrl7Dp+usIOu9tqUeCQg6/qqa5A1nfoPGrtIdny7WW1MT1fWlqaU+f0l2p2Wr8wuBG7jH+FqCv168MyDYjpi1JeC4PenZeuYHb/W1/xsz9dbURN09aome+Hallmw/qNW70rTrUJbT9vrZvG3Ox6Zl5Sktq3iVbroroA/+YbUmrPKGp1ELtuuOTxdr+c5D+teUjbrz0990KDPXaRUtrTK6u5TKlb8q+ML4dbrHt71FUfsP5+qgb9iNf2sIf+j0t4T6K7ib9nnX+K1z7cG4LyNb2XkFeur7VUrJyNFPK7zVy2BTRI8cpTJYktTDOfJ4rAa8OStopTMnv6DUr8PR7D+co+d+WhNwzbn5HvV8ZZozbKgsrLVOi6v/e2vC6j1O5fr1Xzfq9KGTdYdr/WpuvifoEKBgvluSpISB40scTvT21MSA9bf/i2nr9wb8e5Sk6Rv2qemg8UG/t90yc/OLdRm4rd2drvTsE7O2FQAQGqHaRw0hUj4mWh/d2lnfLknS389vqRGzNuvR81rq/n7N5Z93MeqObpq1MUXP/bRG57ap63zsK1e11/hVyc4Y/9F3dgv6HH/r21wF1uqazo0UHWU05NJ2enOK9y7+3b2baltqpk6tUVHvTN+kahVjNehCb7Vu3P09tWznQe3PyFXzupWc6oUkjbmzm7LzClSvapzmJO5X75a1deeni4NWAJ4c0Np3Hc20Lz1bVSrEatr6fbpvzFLnmKa1TtFpDatK0lE32a5QLkr5BYHHREcZ54XYA/2a650i66FOtMPZZX+x7P/7K029KnHOFguLfNUx93rBoiat2aOWdSuX6fmnrd+naf9j9XH/4cAXtZ/7Nl3fnprprEf8bP72Yh/nt3THQSXuCxxC8sDny4ptK7HY9cK06NAXv4yjBBX/1hNLdxzSGcN+1df39HDOnTBwvK7tHK+WdSvru6W79Oo1p6tt/So65/UZQc+VnJZV7PO6b8wSvft/nYode9k7cyV5W0Cttc5gmwKPVerhHH3iC6HBBt10fbEwRKdl5Wn8Sm9QO6W890f6tv1HVKNSOc3fnKp7Ri3R2zd01KVF1quu2Z2mlIwc9W1Vx3cduRr281qd2bi6zmhUTZe8PUcXn15f6/dk6OEvl+v8tvU0fNJ63dunmepWidNjX63Q+FXJ2vTihcrO96hS+cJfJ+e8PkO3npWgm3skBP06SdKQcWs0flWysnIL9MrVp0vyBuBdh7J096glQfd1DObHFbv18JfL9eXd3RXt+1olp2U5ATjT9/c/O3G/PpyzVR3iq+qa9+erR9Oaxc61Znea/v7NSn11T3dVifPemPqX72fj5pTDys23qlYxVm3qF7asv+6rgH52e1e9PS1RX9zV3bkxIXn/fspFR6lCueL7OhZ4rIb8uFq3ntVEzetU0u2feLsGbuha2Onw1tREeax3LWznhBolfh0e/Wq5Jq3Zq2WD+6v6KeUC3met1UVvzVbnU6vr27+dpf/O2qLvliZp4iO95fFYjZyzRdd1aawjOfnKzM1X8zpl+1nhll/g0fwtqerapIbKxxT/XIGT1TvTEtU+vpr6tKwd7kvBHxBBLQKd07quzmntDWBbXg7+YqV3y9qa+njfgMeu79pY13ZupB+W7wo6ROTlq9orIztPFcpF64kLgm8XYCS9fUNHSdLVneJVzVVFS6h1ihJ8Uy2P5OSrTf0qzl3+ns0Lq3ztGngD1ts3dtRV787TVR0bauyy4GuP6lSJkyRd1L6+fn20t/am5+imDxfqQl87o1S47k+Spj3eR+ViovR/Ixdqu28CZfmY6IBNvSXpwtPq6Wffi9e/X9BKfVvVDqgiBXP1mfHOXna392yij+ZuLfX4oq7r3Ehf+drsvr/vLFl5B5/c8eli7S8ygOS7v/XQ6l3pGvLjmmN6Dj9PGdfg+I1esEN1Ksf9rucKh7d9bZRuJe395/foV4VrjTrEVz1qRdJvUJG1edd+EPh94q6gjpq/XV2a1Cg2OOeMYZN1KDNP57lunPj5K3hFuSuL7psUknTHp8HbfINJcg0Xmbc5VRNX79G9owMreQ9+sUzntK6jjOx81apUTjHRUbr4rTmS5ASi2z/5TUt3HNLYpbt01ZkNJckJgJI0cOxKjVu+W98uTtIrV5+u8auSnee8+aNFurFbYz13WTtFG6MtKUf07Lg1AUHt5V/WaUC7eurYuLok6VCW99/El7/t1N29m2r+ltSAtaxPf79KV50Zr+oVY52OgI17M/TQF8s06o5uql25vJLTspxhPW9NTVRXXwfBzgNZGvbzWknSAVcl7HnfY5KcCbWSNGPDPvVtVUf/npKodcnpmpO4Xxe192614p96euvHvznV8g0vDND1Ixboqo4NnXPc9dli5eR7dCAzN+DfWofnJqtprVM09fE+Msbo4JFc3fbJb3rr+o46nJOv0Qt2aPnOQ/r5wV4Bf2fzNu9X3SpxTvg8WgFwyXbvWs0jufnFglqmry3cf2PDP9jJWqt5m1P10oT1Wp+c4fyc3vbKxVq9K03JadnanHI4oLvjlV/Wq1+r2upWJOg2f/oX79ehVxM9fXHbYteXlVvgTID134QLlS8X7VC3pjXVpFbpXRpllV/g0VvTNumOs5v87k4S//Tiq86MV4NqFUJyXfhj8t+cLOvNJ8CN1sc/Gf9G3O47un43dG2su3s3C/JR0hmNqkmS8yJKkprUOqXYL3y/U8rH6JeHewV9n1/HRtU0/JrTNfTydvr09q5HvfYWdSvr7Ba1tOjpc/VY/1bF3m+Mt4UzvnpFzXyicJsD953q1c9doGs6xWvwJd4XCvf08Q5Z6ZxQI6Bl9NSaFZ076i9d2V5f3NVdr1/bQT/c31Ot6lbWY+cXDmDpXca7YM9d3k7LBvfXmucuUMfG1XVm4+pqWN37Czo336M6lctL8v6w7nRqDd3c41TnYz+8pbOG+6oKkpxjg3lyQCtd3zVwbaH7cyuJf+1TfPUKx7yvn9sTFxT/uwmmRgnfO2UxfUPw6lhZlfR9/r/6aeXuoMMn/INRppQwsCZh4Hjd9vEiDftpbdD3F62GuivRR2t5K9qiWTSk+bUbMkndX56q5k//EtD2l5NfoJ0HMrV0R+FQlmCTJcct97ZmZuTk6/7PC4Plzb6JpJ8v3KEWT/+i7a6P9bfx5hV49MHMLbry3Xn6bP42SYFrSl8cv67YwKExC3fo6vfm6ZzXZ2repv3Ocev3ZGiu7+2fXGsU521O1ZtTCgP+LF9LctFQHcytvu4A/4vyQ5neNto2gyc6rcnulubVu9K1bMchDR5XeKPFf1zi3sMa8OYsLdyS6lRrt+w/ogv/PVsLfEOClu88pPdnbVaur+3TyAR8za21uvG/C3Xu6zOdimpJN2d+WrFbiXszVODxnutwTr427MnQoLGrnHV5/psC0VGB1dm0rDxnsM5K18TVJdsP6JK35+iuzxbrlV8Kp5paa/X+TO8a2pJs3Hu42GPWWrV5dqIueXuOLnnbe4Ng+MT1uvTtORoybrVenrBOm/ZlKL/Ao7SsPE0spTsgJSNHz/+8NmA7lIFjV+ky33lDYdKavXpraqI6PDdZL/wc/N9sUWt2pwVM2N2cclivTd4Y8G+lNNtTy77dTlndN2aJnjhOw3IO5+RrZdKhox9YRpv2Hdava0se+HWiWGv12fxtSj3J9zdF5KCiBklS31Z1tPCpc1W3yrFVXSY81Esb9qYHfZ8xRtf6hqocS8k/WOVn0dPnKiYq+H2FCrHR+vbeHsrMLVCl8jF67S8dJEnrnx8QsObOv3XC8GtOd66rqDMaVdOkR3sHPPbZ7V31zrRErdqVpklrvL9IOp1avdh6kLjYaMXFBrb8NHTdSZ3xRN+AwR/G1dJ2bpu62n84R92a1NCdvZpqT1pWwItAv3H391SHRtXk8Vjd3ONU9XxlmnLyPerTsnaZWiYlac4/zlFGdp5e/mW9jJH8r/82vDBArZ6ZWOz4i9vX14qkQ04guL9fc706acNRn+e3p8/Tmt1pTovf8fTEBa0Crql+teNTPfRXJs5oVC1g2mRZTN+QUuYA6t677mjTJYtuF1EWzZ6a4Pw52N/50aqXpen32gznz5e8PUdLB/cPWCf37Lg1uq5LI+drKR190M+NIxfq3j7NnEE+j3y1XPsyskO2blaSPpqzVd/4Bots2ndYHZ6bXOKxpa3N86+fLBpm1u/J0PUjFuipi7w3ST5fuMP5+RAbbfSpa73lB661m/4fE/5gkp6dp+y8AtWpHKdlOw7qQd8AGf/Xwj0ZtnJcjE5rWFXNfRXJAo/V9SMKK8ZnDPtV1SvGOp+zX9EOhaU7DiomyqiFqyUyO69Ae9OzdWrNwCrWpn2HNWrBdt3YtbETDFOCvOh9d4Z36NIqX0D8YNYW3dS9sVIP5+qX1Xs0/e99g1bInvlhlSat2av46hV0W88mzvdy0Rbnaev3KjO3QBe3rx/ws/aZH1apbuU43dW7qWKjo4qFV0na4QrNI+ds1d29m6rrS1P13v+dqQrlotWqXmXVr+r9u/th2S7N3bRf3yxJ0o3dGuulK9tLkvxLL0tre7/7s8XacSBTL1xxmq55f36pv5t+D381/1Xf78TSHDiSq+d/Xqthl7dT5bij/7u6f8xSzdyYovXPDyj2e+/3OO+NmZKOX9Upr8CjdkMm6aUr2+sa35r8YNYmp+vZcWs0a2OKRt7S5X9+3mBLN47k5Dtt6sDRUFGD41hDmiS1bVBFV3Ys+Yee2z8GtNZdvZoc83NI3vBWtELjXxMTFxutzgk1ilW+4mKjA35B++9eN6hatjaUOf/op8/v8q7ze+CcFnr/psJ1RqPu6Kq3b+iojo2rlXoO9y+8iuVinHUvfk9c0MrZPqFWpfL66p4e6t+2rvMi4L6+zbT5pYtUq5L3c69TxVtpi4oyqlWpvFOVy/dYndYw+FYPQy8t3oZUOS5WUx/vo5VDznceKx8TrdlPeiuVPZrW1H19m6lV3cp67S8dNOcf5wR8/M8Pnh3wdkyRFztj7uym6Cij0+OrHXUYTEkuPK2w/fX8tsVbCt3qFfnerVlCNe/f15/xu66lqPv7Ndf1XQJfUPn/jv4X7lY6P/+L5kfOa/G7zjnhoV6Kr35srVelDVI5Vpf/Z06xLQXOfX1mQPWhLN6fuTkg3L00YX3Qmxm/1zBX5eRobc8P/g/TNd377vlvLsRGRwV0L7irWP6Kmv9zP+/1mer64lTtTc/Wle/Oc44LVnkdMWuLHvpimYa6WqwXbAmcJnswyDYZRYfSXPXuPF32zlwt21F4c+qZH1arz6szlHo4R2muc+w6lKXBP6xWs6cmODcZLi9ys6akwSyjF+zQZF9VpWhF4+vFO3X+v2Y601mf+2lt0G0YMnPz9e8pibr9k8V64PNlajJogs55bYb6+4LA6AU79PqvG9V68ERnXWhR/5wYuDeiv43+b2OW6taPf3PahiXvTQN/wN/g2lbGyvr+H2j5zkPO13GybyuaLSneatqUtXu1cW+G/vHtShV4rJbtOChrrbc6N2lDqdtOJKdlBeyFmeEaHOMPC9l5BXrimxVav6f4zdW3pyXq+2W7St0uxM0/Obise1Qu2nrA+bvze2daosYt36XEvcW34/ls/jZd8K9ZZTp3WaRleSe/vjC+9AppmtMdsS9g8vHvlV1kK5hN+zLUbsgkfb+s8Ou8Ljk96HCjZ8etVvciQ732HsNNtNs+XqT7x5Stonsi7EvP1sjZW8q8fQq8CGo4Yf7Wt1nQtQu/1w/399Tzl7cLekc0mCGXttNVHRuqS5PqRz9Y3s3Jz2pW2FLoDn0Vy8Xo0g4N9P19PY96nmcubqOPbwt+Z+7+fs310LnFX4Cf26aOPrylsx7r31LRUUaDLmyjyuVjVLtSYEtkbV+LZKXyMfr23rM07v6emvxob2fDcEm6tWcTTXykeJtqs9qVit05bVSjoj69vav+e0tnPTmgtSY92ttpLb3qzIZOS6l7jcn9/Zpp5pP99PU9PVSrUnnd17dZwJrFWq5rLss01MvPaKAuCdX1lm+tpCSNuLmz8+eNL1xY7GMauoLI/EHnqFrFwhe9jWpU0BvXdtA9fZrq8jMa6v2bOunzu7oFBMFOp1bXdZ0badng/kfd2/Ce3k3Vv21dndk48PuoQ3xhaL/ijAZ6/6YztW7YgFJbhJ8cUNhGuvzZ/nrjupKD5CWn11dF39/FrWcllHqNfpXLx6htgypHHchTVGm/R93rlcrCv6m9W9LBLG1L/d837g42cl+SLmhXerCPNLHRUUEn7kqFa+n8L4j3+aqt3Y5hKuuiIluKHE16VvAq0I2+aqEkZ1uDTi9MUYdhwauP/i0silboSluD6X/BOvSnNU74PHgkV09+u1Ib9x4OqHYV3TszO69AbZ+d5AyA8duy/4gS9x0utpfmTyt2Kye/QE9+u0K/bTugv364UC8GeSE/rEj744Ejudq2/0ixYVnuNd13lvA5XvGfubry3XkBbbtPfrdSkje4XfDmLH21eKc+nrtVV747T98t3aWbP1ykd6ZvUpNBE2St1TeLd+qHZbv08JfL5PFYfbloh3q8PE0Dx67SkHHeFmL3C/ymT03QoLErNWnNHn2zJEkDvyu+9Yj/39IPy3cXmzwajP9Hyj++W6ncfI827s3QzI0p+mVVcrFjCzxW134wX+e7gldOfoFem7xRD3+5XP1dj/t/Vj07bo027M0o8WdXTn6BrPWG2W+KbPPiNnNjilYlpTnfV8F+tq3dna4PfNvquG8GHfH9eeeBTL0/c/NRA0Z6dp5WFOm0KBpk/aHcP6l3494MXfjv2fp3ke9ZyTtwy93dMGXtXnV7aWqJg7OKmr4hxVlLHGqph3N0sEjwzsjO020fL3Km8Bb1tzFL9cL4dc58gaKstXpx/FqnZR5e1F7xh9W8TqVj2gaheZ1Kpb4QLovruzQKCCGSNOWxPtpfSj/7nb2Cb0ZeGmNMwETPqzvF6+og7Rq39WyiuNhoXd+lkWKio9TBt9bwrt5NNfiH1U5AS/C1J5W2TYBfSW2qb1wb+LWb8lhvlY+JViNfKGxYrUKJ1bMpj3kHxZzVzLsu8NN52xQTHaVW9Spr6/4jKvBYPf39KnmsdHOPU9XpVO9QiM/v7KadRX7ol4uJ0r19mgXsV9cloYZG39FNv6xOVt3KcYqKMvr23h56+Zf1eu6ydgHBcoAvoJ3VrJZmJ6borx8uUmy00T+v8a4R/PCWLpq8do8mrt6j6Ciji9rX1zWd4tVn+HTtTsvWXb7N5YsOYzyvbV1NXb9PL1/VPmBqX5v6VXTH2U3UtUmNYuP6uzWpoR8f6KlyMVFOuJz0SG+9OGGdejStGXBnP8oY3dwjQe/P3OwEdEk6rWEV/ffmzurxsndbhovb13d+OftbnnanBb8L+9xl7YoNtLnwtHrF1sxVjotx1mg9cl6LYnsFhkKVuBjd2rOJsx9fuegopwp+rBpVr3j0g3zu79dM/5nu/Xwa16ioelXiigUb9+ffpNYp2ro/tOuJsvMK9ML4daUes3pXmr5ZUvIL0lD6vUOOgjna/pYlWb0rXR2em6wfH+ipkbMLK5zuPRzdWy5I0odzSq+EBguIr/yyXl8vTnKGBpW1hbyvq8XXr8BarUtOV81TyjlV6V0Hs5SclqUeL08LWBv8nxImEfuzgP/7YWXSoYBAuHTHIT3x7Urn7YOZedrsalv9dP529WhWywkZfl8s2um0kvrjxqyNKcrIzlf3pjWc6ufynYe0fOch3dC1sTweq5W70hRfvYI6vzBFH93a2Rl05q8YTl67V/M273fWeUren0nZeR79/ODZiouNdlq53b8nM3OCV+Jy8j0B684zsvNVtWKsPl+4QzM37tMHf+2sWRtTdPNHi/T0RW2cwTgFHqu+reqoXtXA7opbfGtoZ/nWtQcLW1e+O1c5+R7d1rOJDrkq00dy8lW1Qqxu/+Q3Je47rCs7Niy18+i2j3/Tku0HNXegd3lB63pVim0b5G959LfE+itki7cfVEpGjqpUiCk2NdVaK2OMftvu/bm0aleaerUo+3KSYT+t1ePnt1Ruviegcp+dV6By0VHOwLaOwybrrt5NdV/f5kc9Z6cXpkgKbFUdvzJZ0zek6K2piRp+TfF2W/9+rlv2H1bf12ZowkO91NZ1Y3TuplT9d/ZWfb04SStc3T7HInFvhvr/a5amPNb7d02vjUQENeAYvOIa+OF3rIExlGKjo4KOQL+pW2Nd1qGBs24lLjZaH97SWe3jQzdt7Vh+CDavUzng+Ft7FrbAdvGNHL++SyNtT810JotK0llFQrF/Tc+TF7TSvX2a6oxh3v3aoqOMzm5RS2e7hqp0Tqih7/52VqnX5W+nrVah8JdXvapxurlH8dHyI2/pogVbUp0K4UXt6+vXtXvVu2VtPTtutc5rU1fXvtQoaIXXX4lcNfR8jVm4Q6/8sl59WtZ2Aqlbq3qV9Zlv+M4lp9d32gYbVKug23omaG1yum7o2livTtogY1RsWuC/rz9DF5xWT5e0rx8wMbWoH+7vqYbVKmjIj2t0as2KuvrMeNWpXF6NalQsFtTG/u0s5653SZUfqfhawWOxYsj5Msbolh6nam96jlrVqxywlq6o1//SQY+XMCihaKvnG9d20GNfBz/2ovb1tWFPhqas26dzWtfRkEvbqsmgwOftEF9NczbtV61K5fTl3d1LrGZVrxgbtJVQkt66oaMeKqFlcnEZgsvIo4SQ46FrkxpqW7+Ks1XE0TSrfYo2pwSG2BfGr1WtSuWKbdlRVr+s3uMMPClq7qbA9Zu/53vv47nbfs9lBTVjQ4pmFFmHmpVX4NxEednV0rp+T/F2v2CKbvdx9XvzAt6etTGl2M+cYAOFKsRGO+tq/VUq/yCgYK4fMV9nNKqu92du1l+7e1vs35ySWBjUXHmn6Oeyepe3tfK9GZv1aP+WSk4rDJqph3P00dyt2pMW/OZm6pEc3fJ24XWlZeVpVmKKnvreWwXMzfc41+0PaZI0cOwqdYivqrt6N9XcTal6+ar2xc4rSenZ+Rr64xrNTkzR+Id6aVvqEacyeygrVweOFF6XfwiPf0BSRna+6roaLqat36uJq/c4ocR/A6Knby/TFUPO19n/DGz79rcVL91xUGe9PFWP+pY+HDiSqy4vTtHZzWsV21opK69AFcvFOF/z6CJ3CW/9eJHOb1tPN3YrvEHorop+NHerJq3Zo12HsjT9730VG23UoGoFtR48Ubf3bKJnL22r7LwCHczM0/CJGzR8ovff0df39HAm6RZ4bNDfbRnZedqXkaNmtSs5X6+K5YJHC39o/dlXTfx55e6AoHbTh96KfZAdaUq0JeWwtqQc0Xlt62rngUznBsikNXsJagAilzHFNyg/N8joeEla9NS5ZW4fPZ6MMQEhragVz56v2BjvdUZFmYD2xt+rbf0q+ufV7XV+23pHP7ZBlYBfKqeUj3FaMm/qfmpJHxagclysOvqqnhefXv+ox8dXr6DruzTSNZ3inWE1/hC3+JnzFOsasFMhNlrRUUYx0VFBt+eQpKcvaqMezWqqUvkY52v9/X1nqWntSs73S7BNs1vUrayXrmyv1MM5MsaU+ML7/n7NlV9gNWrBNo25s7sueLP4GpPZT/ZzwufMJ/pqxoYUNatdyWktrlmpvGoWafHd+MKFavmMd/z7nH/0U36BVfVTyqlbkxrOxuBu7n3OZj/ZT/HVK5QY1JrXqaTz2tTVlHX7VOOUcjLGqHmdSs5wjVvPSlC/1nU0Z9N+pWfnB9xRb1u/itYmp2vR0+fqy0U79UA/753opr6A+ewlbfXFoh0afWc31a0Sp6oVYp07/CW59awEJxj1bF6zWBiRvC2z/hdTfrf41qt+Wsr+hH5FK9Ilee2aDjqQmVumoHZKuWh9cVd3rUlOD9xjc2FgG919fZs5g0T8LmhX1xnUVNR7Mzbrpu6Ng74PXmXdvN0/XCQ7r+Co7Y0Lthxw1jOOWuD9nlqZlKY2gyfq63t6BLSdutdUuqUeydGkNXsCOgn8lZiSDPtpbUDYP5iZG7AmtLQ1cSuS0vTA595jT2tYRatcW7S413P6v59v/nBRQPV8X3pOwBrSmRtSVDkuxmkL/XDOVg29rK3Kx0Rr4ZZUZ9/DmpXKa0C74r9D/usaCiR5p/+eWtNb7c/3WO1Oy9bYpd4tMfxhd86m4hXduz9bopu6n+r8Pft/X/vX0PlvEFzZsaGe+n6V7u3TrNi2M/6qrH/Y08KnzpUkfTp/m569tG2x9YOSd9uLlnUraeeBLF36zhz98+r2uq5LY2eareSdmLtk+0GNve8sp220YrlotXh6ggZe2EZ3nF14U9b/d7c2ufgaSfd6wJioKOUXePTFbzuVnpWne/s0076MbPV/Y5beuLaDznd9rQeOXaVFWw9oymN9nIE0kvfnkeRtkY02Jugk9D8KghpwkqvzO4bIhEPVisUnkX18W5cyD4cJxhij67qc2BeB3ZrW1Iy/93V+YZfGGBO0iisFrv2TpGXP9i/xPL8+2lu7DmU5m1u7dSyy1i42Okqf3t5VleNidN0H85Xn20zefbd27sBzlJ3n0XM/rtE/LmytlIwcZ7Ljw+e10MO+wSfPX3GaBv+w2mlR6tWiltMqK3lbDW8pw3q7cjFReujcFnpraqLiXW2NX93TQwkDxwcce0uPU3WGb8jPKeWiVb9qnIwxWjHk/KCTHMvHROuaTvE6nJOvv/rCzug7uqn7y96q2dDL2jmj8xv42qo+u72r3puxWR/e2lm5+R5Vq1iu2FrTG7o26yNAXwAAIABJREFU0u1nN9HtrhcqRe+Ef3xrF41esF1T1+9T4xoV9cFfOznte0MubatbeiRo0bYD+veUxIB93249K6FYUHvu8tM0ekHxkPa3vs30XpFgdFmHBk5QO6VcdLE2OedrExulVnUL70o/3r+lRszaotiYKB04kqtyMVHKzffohq6N9NKV7WWMUZ0qcVr93AV6cfy6gDDQIb6qLjujoS4/o0GxoHZ918YlBjVJ+nJRYcvn9V0a6cvfjr0FtH7VuGJr5cLt4tPrB+xVeDy5W/AS9x0u9kL+WM5z6Ttl2wph9IIdGr3g6Ovd3CYXGdE/cc0eVasY62yBMm1D2Ub4F93uI5iiLc6XFNni4cUJ6/Sma+3YF4t2qF6VOD18XouAauR7MzYX+zcmSe8EaW8tuj7rYAmDddzmbNqvOZv2O2uTXxi/Tn1b1dFl78xRv9aFP9N/WZ2s75ft0vcl7FvrdsN/vetHCzxWw34Kvi6sasVY9frndGei6qCxq9StSc2AtYb+KuL21CNOUMvN9yivwOr5n9fqzV83qnuzmrq2cyPne9AfSt+dsVm/bTug37Yd1Ds3Fq5Jj402Gr8qWYN/8P4djlmw3Wnfv3vUkoB2S/8An2nrA78vyvnaR1s9M1Fdm9TQh7d01tilu/TX7qeW2mkSiQhqAP6w+gUJHn8EpVUOf6/SRmS3qFtZLeqWvQ3Ev05xzj/OUXqQaYLlY6JVPibaWfNZ0rqNv3Y/1WmbuqZTvLM+YtCFrTVhVXLAgJ5g3BWzx/q3dCakluSCdnX13OWnSSo+5rtqhdiAypxUuG4lJjoqYC1pvapxGn1HN9Wr6g3DjWpU1CtXtXf+3nq3rO1MmQ1W2N380kUK9lrAPaimfcOq6te6jno0q6m3pibqio4N1bJuZXVvWlPfLklS75a1FRVl1L1pTXW6o7pa+DaU7tm8piqWiwnY1P3SIhXUlnUraePew6pYLlpdEqrrPXkrrv4XSpXjCn/1f3VPD70zbZN6Nq+pJrUqyWOt8yK0XHSUKpSL1taXL5K13kr2A+c016gF2/XsuDUa+7ezlJyWrf5FprJWKh+jF684TdUqxjovYK/v2lg3dG0cdI1QxdhotW9Y1RnVX5R/W5MNLwxQlDEBQe3v57dU7crl9Y8gAzJ6NK3pBNyRt3QOmNbod1azmhp9RzdNWJ2sz+Zt16JtB/TxrV303szNWhSkWjvpkd5BK8V+N3VvrFW70osNlZCKVw67N6lx3ILajd0a6/OFxxaS+ret6+xlFl+9Qminv57RQGt3pytxX+A+e3f3bqoRRSpPbl8u2hGwxcGjXx2ffeFKUvQmxr+mbFTbBlWKDbL5vYK1wKZl5imnoPjNE3fl9Nlxq5WZWxDw/VNS10AwW1xVy5Km3BZtC/ZYBYQ0txU705xKn7tNOyMnX7+u3VviHnm/bfMGvX/9WhiIjbxTN/1KWmNd4LHOwCH/efz+M32Tpvm2fVm09YDuG7NUsxP3q039Kk475x+FCdeYzM6dO9vFi0ue/AQAOLkVeKystaW2rfgrap/e3lVnNq521D2g5m7ar/8buVDPX3GaEyJPtIzsPJWLiSo2NEDyDg7wr0lxS07LUlxMtBN292Vkq/fw6fry7h46w9dOO3rBdj3zw2pd1qGBflyxW3WrlNeCQedq6Y5DOrNxNa1MStOIWVu86+W+XKYL2tUL2ibr/5quHXZB0PUm/uEGZeE/1+aXLnJeyI1asN27D5qvRXLc/T3Vsm5lHcnN1/+zd+fxUZbn/se/V/aFLJAECGEJIqsgW4S4L3VBbRWrVcHiDp7+XKo9tVW7aK1trXb1HE9bcLcIdaVaF1pbbasVZBUEBJE1hDWQBLJncv/+mEmYJJNkgElmknzerxevyTzPMzPXhIcJ39z3c921Hqc653SK7zoff/Xhe3VBScOozqafXiRJ+v6fP20IJtdMHqjROWm6Km+Atu0v17qdpTp7RG+N+IF3zUD/dTD9A/2jCz/T4+99ob/cfpp6p8RrUpNrES8Z208PXz5Go364UBMGpjcsEp+Tnqi4mCht3lemH11ygq46aYAeXbheT36wWbeceZxS4mN02zlDVVXrabRu4Zxr87Rp7yF5nNPv3vtCB6tqddGYvvrNVeMb/UJB8jax+dtdZ+h4X2Bf8r1zNXfx1kaLvH91Qo6mjstRv/REDc5M1o4DFar2ePT9BZ82W5ZB8gb2ujqnsmqPzhvVR3/4+kT954sijRuYrmf/s+WorzeVvNfKfnP+4eUPrj8lVyOzU5oF6kDTeMNtaO8ezQJlRxqdk9pwnZ+/ownfXVF8TJQye8Q367oajD/MmKgLAkxTDTczW+acywu0jxE1AEBE8v7HvvVA8OE956joUJVO7N/6mob1Tj0+U2/ecZpGZbe+DEN7ai1MmlnAcJTdZIpv75QEffbjxktVHJflHfGbfFwvjcxO1fkn9JGZaeIg7/TWsQPS9fg1EyRJj0+f0GINYwek65PtxYprISAHG9IkacUPzlNcTOOFpesD8sBeSdq2v1xOUmJcdKNuf5k94ht1Cfzhlw8v7TKsbw99aURv3XXesIZpTD+9bIzuu2ikvvvKKt3xpaENo7y5mckNI6Hv3Hm63l69S7edc3zDCKW/u84dpnNG9GnoEjt+YLpW+MLYg5ee0NBg6O//fab690zUT95cp+c+2qqbThusd9bs0uZ9ZRqcmayE2Gh976KRuuXM49Q75fBoc3xMtJ66Pq/h2qbstISG0cj84zK0ZkeJZvhe4xdfG6uMHnFKjovRlX/4SD/8yqhGv7DI7BGnqeNy9Jt3P9eVef31X2cOUf+eSYrza/Yz0De9OtBo+zt3nq4RfVPlqXP67bsbdO0puYryNWWSvMu3BBLs1NNLx+U0CmoDeiUpL7f5SMY5I3pr7qJt+u3V47SqoEQP/mWtTsrt2WyEpDUvzJysn761LmC4CVb9OS9Jv58xUV/65T8b7feffhlqqQkxKvUbNWzpfazc1niUtj1rimRVtXVHFdIkqfAoHxdOBDUAQKeVk57Y0A00WCf0C13300hyypBM/fWuMzS0d48jClNNPXfjJG3ccygkF+D3bGHxeUn6ythsPf7eFwEXi3/rjtO0bX+5rvj9R5LU6Fq/+JhoPXl987Upe8THtBpAR/RN1Yi+3oD+hxkTGxrG1IuJjmoItZI0b2a+bp27XH//bE/D46TDIWaML9ClJcaqf89EfbxZDS3wo6KsUUirV981UfJ2cq03YWDPRmszXuG3HIv/qN8fZkzUzuKKhuZLTaf4BpLepLHUxp9c2PB3Gx1l+tb5w5s95rShmTp7eJZio6P017W7NSN/kG4+fbAG9krSjy49QZ/vPqTUhFhV1noaTYf76WVjNGFQ41+azLk2T+eM6K3oKGsUiCRpWO8UfXjPOZK8jX08dU5fzx+k+Uu26Stj++mlpQV6edl23XPhSM18LvAsrFOGZOqeKSMbugZK0mnHZ2r1jpKGqXH3XTRCr60o1FfH5zTqFjlt0gDN+3h7Q/MJybucjX/DnSvz+uuhqWOajXJef0quPikobgjzTX+54G/+rHxdPXuR7jp3mF5atr3RtNI37zi9ocFSa5o24Tjt+MyGxdjrjcxO1boAzTok7yjsyu3F+tXfNrR4zNHISU9sFpxuOeM4/aGVKa3eJSGObH3HUOiMQY2pjwAAoMN56pwKiysaNZhpqn7qZDCBpD3sL6vWayt26MZTc5uFX+ec/v35Pp12fKbKazz6dEeJ8o/LaPM58x76m/Ydqtbmn110TIE6WMu2HtDlv/uP7rlwhPqlJ7bYFfZo1HrqdPz33lZibLT+75oJjZpbvLR0u+JionTpuJxGj5nx5GIt33pAebm99Kyvi20wKms8MvOOqPSIi2norrrl4YvlqXMNy3m8c+fpys1I1jf+uEzvrd+ri8dkN4wkv7d+T8OU29OHZurb5w/XpY9/qO9OGaEJA9O1q7RSl47LUY2nTgtW7NDdL6/SX+86Q8P6pKi4vFpXz16kGScP0pdP7KfUhBi9uXqnbnthhV6/7VSd2D9dawtLddFj/25U98f3falZ06768/r/rpmgi8ZkN9y/+4LhGpSRpG+9+Imqa+t0xrAs/WvD4SUfrj8lV//csFeb95Xpu1NGNFprs/578Y0/LtPbn+5SbkaStvg1L6n/N7RxzyGd+6t/KislvmGNO8m7XE2g7o9N/fs7Z2tNYWnDMhBbHr64WVOnW88eosLiSv1lVaFqPE7jBqQ3LA+RkhCj1Q9coGmzF2lVQbHWPDhFb67aqVtfWN7oOc4enqX31re9uPfUcf20YGVhm8dJanQuRJLWpj4S1AAAQETaWVKh6to6DcoIfQOecCksrtCWfWXN1onsrBau2aUxOWmNRgg7QtMQf+MzSzSsT4ru8S0sfrCyRut3HWw05dI5p7dW79J5o/o0TBPdvr9c/XsmBgzNlTWeVhs1Sd613vyXw5nzr00aOyBdD7+9Tv99/nCdGuDv+dsvfaJ/fLZHy39wXqP38so3TtHEQT0b2tNflTdA63cd1Pwl23T7OUOVlhir8upa1dY5/X3dHt332mpdPqG/XlleoL6pCVp035e0v6xaJRU1qq6t0yvLC7RuZ6kmDuqpO89t3Izphqc/bhSE1j04RXMXb9VDb67Tt88fptE5aXr2P1uahaX6602f+Pcm9UtP1EVjsvXEvzc1LNIuqSG4VlR79OBf1ujuC0aouLxa5/zyn7r6pAF6+PIT5ZxTnTu83EDTsPezr47Rva+u1oWj++p7F4/UL/+6Qa+t8HZufGnZdlXWeBu6bPrpRfrL6p3y1NWpvNqjt1fvCrjMgSSNG5CuBbee2urfZzgQ1AAAANBlhHu0NZQ+3VGitMTYVkeXm6rx1GnOvzdp+qSBGvfg3/TdKSP0jbOGBP34ggPl+voTi7WlqFynHp+huTfnyzmnqtq6RuG01lOnOif94q/r9dQHm7XR18CnqVvnLle/9AR97+JRAfdL0rKt+zU6Jy1gI6X3PtujG57xjnYu/f65yvCbNm1mDWvyzZ4xUSu2F+t373+hUdmpeuubpzd6np+/81nDvrU7S/XmHafp4sc+0Lkje+v2c4Zq7IDgrmfuSAQ1AAAAdBm597ypEX1T9M6dZ4S7lLA7kk6s/g5V1eqK3/1Hj1xxYtANmdpTW+G7vmFPWVWtnv5ws6ZNGqiMJmuKVtfWaeGaXbp4TLbKazzqER/57TgIagAAAOgyKms8ijJr1OkSnVtXGiU9ErTnBwAAQJfR1rVj6HzGDkjXntLAC1x3VwQ1AAAAAGH15whs9BFujBcDAAAAQIQhqAEAAABAhCGoAQAAAECEIagBAAAAQIQhqAEAAABAhCGoAQAAAECEIagBAAAAQIQhqAEAAABAhCGoAQAAAECEMedceF7YbK+krWF58dZlStoX7iLQLXCuoSNxvqGjcK6hI3G+oaO017k2yDmXFWhH2IJapDKzpc65vHDXga6Pcw0difMNHYVzDR2J8w0dJRznGlMfAQAAACDCENQAAAAAIMIQ1JqbHe4C0G1wrqEjcb6ho3CuoSNxvqGjdPi5xjVqAAAAABBhGFEDAAAAgAhDUAMAAACACENQ82NmU8xsvZltNLN7wl0POj8z22Jmq81spZkt9W3rZWZ/M7PPfbc9fdvNzB7znX+rzGxCeKtHJDOzp8xsj5l96rftiM8tM7vOd/znZnZdON4LIl8L59sDZrbD9/m20swu8tt3r+98W29mF/ht5+csWmVmA8zsPTNbZ2ZrzOybvu18viGkWjnXIuazjWvUfMwsWtIGSedJKpC0RNI059zasBaGTs3MtkjKc87t89v2iKT9zrmHff+Yezrnvuv7ILhd0kWSJkv6rXNucjjqRuQzszMkHZL0nHNutG/bEZ1bZtZL0lJJeZKcpGWSJjrnDoThLSGCtXC+PSDpkHPuF02OHSVpnqRJkvpJelfSMN9ufs6iVWaWLSnbObfczFLk/VyaKul68fmGEGrlXLtSEfLZxojaYZMkbXTObXLOVUuaL+nSMNeErulSSc/6vn5W3g+F+u3POa9FktJ9HyJAM865f0na32TzkZ5bF0j6m3Nuv+8/L3+TNKX9q0dn08L51pJLJc13zlU55zZL2ijvz1h+zqJNzrmdzrnlvq8PSlonKUd8viHEWjnXWtLhn20EtcNyJG33u1+g1v+ygGA4SX81s2VmNsu3rY9zbqfk/ZCQ1Nu3nXMQx+pIzy3OORyr23zTzZ6qn4omzjeEiJnlShovabH4fEM7anKuSRHy2UZQO8wCbGNeKI7Vqc65CZIulHSrb/pQSzgH0V5aOrc453AsfidpiKRxknZK+qVvO+cbjpmZ9ZD0iqQ7nXOlrR0aYBvnG4IW4FyLmM82gtphBZIG+N3vL6kwTLWgi3DOFfpu90h6Td7h8d31Uxp9t3t8h3MO4lgd6bnFOYej5pzb7ZzzOOfqJM2R9/NN4nzDMTKzWHn/4zzXOfeqbzOfbwi5QOdaJH22EdQOWyJpqJkNNrM4SVdLej3MNaETM7Nk38WpMrNkSedL+lTe86q++9R1kv7s+/p1Sdf6OljlSyqpn+YBBOlIz62Fks43s56+qR3n+7YBbWpyDe1l8n6+Sd7z7WozizezwZKGSvpY/JxFEMzMJD0paZ1z7ld+u/h8Q0i1dK5F0mdbTCiepCtwztWa2W3y/iOOlvSUc25NmMtC59ZH0mvezwHFSHrBOfeOmS2R9KKZ3SRpm6Sv+Y5/S96uVRsllUu6oeNLRmdhZvMknSUp08wKJN0v6WEdwbnlnNtvZj+W94eMJD3onAu2YQS6kRbOt7PMbJy8U3y2SLpFkpxza8zsRUlrJdVKutU55/E9Dz9n0ZZTJc2QtNrMVvq23Sc+3xB6LZ1r0yLls432/AAAAAAQYZj6CAAAAAARhqAGAAAAABGGoAYAAAAAEYagBgAAAAARhqAGAAAAABGGoAYA6BTM7JDvNtfMpof4ue9rcv8/oXx+AACOFEENANDZ5Eo6oqBmZtFtHNIoqDnnTjnCmgAACCmCGgCgs3lY0ulmttLM7jKzaDN71MyWmNkqM7tFkszsLDN7z8xekLTat22BmS0zszVmNsu37WFJib7nm+vbVj96Z77n/tTMVpvZVX7P/b6ZvWxmn5nZXPOtbg8AQCjEhLsAAACO0D2Svu2c+7Ik+QJXiXPuJDOLl/Shmf3Vd+wkSaOdc5t99290zu03s0RJS8zsFefcPWZ2m3NuXIDX+qqkcZLGSsr0PeZfvn3jJZ0gqVDSh5JOlfRB6N8uAKA7YkQNANDZnS/pWjNbKWmxpAxJQ337PvYLaZJ0h5l9ImmRpAF+x7XkNEnznHMe59xuSf+UdJLfcxc45+okrZR3SiYAACHBiBoAoLMzSbc75xY22mh2lqSyJvfPlXSyc67czN6XlBDEc7ekyu9rj/iZCgAIIUbUAACdzUFJKX73F0r6hpnFSpKZDTOz5ACPS5N0wBfSRkjK99tXU//4Jv4l6SrfdXBZks6Q9HFI3gUAAK3gt38AgM5mlaRa3xTGZyT9Vt5ph8t9DT32Spoa4HHvSPovM1slab280x/rzZa0ysyWO+eu8dv+mqSTJX0iyUn6jnNuly/oAQDQbsw5F+4aAAAAAAB+mPoIAAAAABGGoAYAAAAAEYagBgDocL7mHIfMbGAojwUAoKvgGjUAQJvM7JDf3SR5W9N7fPdvcc7N7fiqAADoughqAIAjYmZbJN3snHu3lWNinHO1HVdV58T3CQDQEqY+AgCOmZk9ZGZ/MrN5ZnZQ0tfN7GQzW2RmxWa208we81vrLMbMnJnl+u7/0bf/bTM7aGYfmdngIz3Wt/9CM9tgZiVm9j9m9qGZXd9C3S3W6Ns/xszeNbP9ZrbLzL7jV9MPzOwLMys1s6Vm1s/Mjjcz1+Q1Pqh/fTO72cz+5Xud/ZK+b2ZDzew9Mysys31m9ryZpfk9fpCZLTCzvb79vzWzBF/NI/2OyzazcjPLOPq/SQBApCCoAQBC5TJJL8i7sPSfJNVK+qakTEmnSpoi6ZZWHj9d0g8k9ZK0TdKPj/RYM+st6UVJd/ted7OkSa08T4s1+sLSu5LekJQtaZik932Pu1vSFb7j0yXdLKmyldfxd4qkdZKyJP1ckkl6yPcaoyQd53tvMrMYSW9K2ijvWnEDJL3onKv0vc+vN/meLHTOFQVZBwAgghHUAACh8oFz7g3nXJ1zrsI5t8Q5t9g5V+uc2yTvotJntvL4l51zS51zNZLmShp3FMd+WdJK59yffft+LWlfS0/SRo2XSNrunPutc67KOVfqnPvYt+9mSfc55z73vd+Vzrn9rX97Gmxzzv3OOefxfZ82OOf+7pyrds7t8dVcX8PJ8obI7zrnynzHf+jb96yk6b5FviVphqTng6wBABDhYsJdAACgy9juf8fMRkj6paSJ8jYgiZG0uJXH7/L7ulxSj6M4tp9/Hc45Z2YFLT1JGzUOkHckK5ABkr5opb7WNP0+9ZX0mLwjeiny/hJ1r9/rbHHOedSEc+5DM6uVdJqZHZA0UN7RNwBAF8CIGgAgVJp2p/qDpE8lHe+cS5X0Q3mn+bWnnZL619/xjTbltHJ8azVulzSkhce1tK/M97pJftv6Njmm6ffp5/J20Rzjq+H6JjUMMrPoFup4Tt7pjzPknRJZ1cJxAIBOhqAGAGgvKZJKJJX5ml60dn1aqPxF0gQz+4rv+q5vynst2NHU+LqkgWZ2m5nFmVmqmdVf7/aEpIfMbIh5jTOzXvKO9O2St5lKtJnNkjSojZpT5A14JWY2QNK3/fZ9JKlI0k/NLMnMEs3sVL/9z8t7rdx0eUMbAKCLIKgBANrLf0u6TtJBeUeu/tTeL+ic2y3pKkm/kjfgDJG0Qt4RqyOq0TlXIuk8SZdL2iNpgw5fO/aopAWS/i6pVN5r2xKcd82bmZLuk/fauOPV+nRPSbpf3oYnJfKGw1f8aqiV97q7kfKOrm2TN5jV798iabWkaufcf9p4HQBAJ8I6agCALss3ZbBQ0hXOuX+Hu572YGbPSdrknHsg3LUAAEKHZiIAgC7FzKbIO2WwUtK98rbg/7jVB3VSZnacpEsljQl3LQCA0GLqIwCgqzlN0iZ5px5OkTS1KzbZMLOfSfpE0k+dc9vCXQ8AILSY+ggAAAAAEYYRNQAAAACIMGG7Ri0zM9Pl5uaG6+UBAAAAIKyWLVu2zzkXcBmZsAW13NxcLV26NFwvDwAAAABhZWZbW9rH1EcAAAAAiDAENQAAAACIMAQ1AAAAAIgwLHgNAAiLmpoaFRQUqLKyMtylhEVCQoL69++v2NjYcJcCAIhABDUAQFgUFBQoJSVFubm5MrNwl9OhnHMqKipSQUGBBg8eHO5yAAARKKipj2Y2xczWm9lGM7snwP5fm9lK358NZlYc+lIBAF1JZWWlMjIyul1IkyQzU0ZGRrcdTQQAtK3NETUzi5b0uKTzJBVIWmJmrzvn1tYf45y7y+/42yWNb4daAQBdTHcMafW683sHgI6yYMUOPbpwvQqLK9QvPVF3XzBcU8fnhLusoAQzojZJ0kbn3CbnXLWk+ZIubeX4aZLmhaI4AAAAADgaC1bs0L2vrtaO4go5STuKK3Tvq6u1YMWOcJcWlGCCWo6k7X73C3zbmjGzQZIGS/pHC/tnmdlSM1u6d+/eI60VAIAO98wzz+i2224LdxkAgFY451RSXqONew7poy+K9Ponhbr/9U9VUeNpdFxFjUePLlwfpiqPTDDNRALNzXAtHHu1pJedc55AO51zsyXNlqS8vLyWngMAgGY68/QVAMCRc86prNqjvQertPdglfYdCnzr/bpa1Z66oJ63sLiinSsPjWCCWoGkAX73+0sqbOHYqyXdeqxFAQDgr376Sv1vRuunr0g65rA2depUbd++XZWVlfrmN7+pWbNm6emnn9bPfvYzZWdna9iwYYqPj5ckvfHGG3rooYdUXV2tjIwMzZ07V3369NEDDzygzZs3a+fOndqwYYN+9atfadGiRXr77beVk5OjN954gzb8AOBTUR++Wgpehw5/XVnTPHxFmZTRI15ZPeKVmRKv43unKCslXpk94pSV4t2elRKva5/6WDtLmjdt6pee2BFv85gFE9SWSBpqZoMl7ZA3jE1vepCZDZfUU9JHIa0QANDl/eiNNVpbWNri/hXbipv9prSixqPvvLxK8z7eFvAxo/ql6v6vnNDmaz/11FPq1auXKioqdNJJJ+niiy/W/fffr2XLliktLU1nn322xo/39sg67bTTtGjRIpmZnnjiCT3yyCP65S9/KUn64osv9N5772nt2rU6+eST9corr+iRRx7RZZddpjfffFNTp04N9tsBAJ1OVa1H+w5VewNXfdgKGMaqdaiqttnjzaReSXHK9IWs3EHJDcGrflv91z2T4hQd1XZDpu9OGdHol3ySlBgbrbsvGB7S995e2gxqzrlaM7tN0kJJ0ZKecs6tMbMHJS11zr3uO3SapPnOOaY0AgBCqqXpLMFOc2nNY489ptdee02StH37dj3//PM666yzlJWVJUm66qqrtGHDBknetd+uuuoq7dy5U9XV1Y3WQLvwwgsVGxurMWPGyOPxaMqUKZKkMWPGaMuWLcdcJwB0tBpPnYoOVTca6WppGmJpZfPwJUlpibENo1xj+qf7RsHiGka9MnvEq3dKvHolxykmOqiVw4JWP+Ois06bD2rBa+fcW5LearLth03uPxC6sgAA3UlbI1+nPvwP7QhwTUFOeqL+dMvJR/2677//vt5991199NFHSkpK0llnnaURI0Zo3bp1AY+//fbb9a1vfUuXXHKJ3n//fT3wwAMN++qnR0ZFRSk2Nrah/X5UVJRqawP/BwYAOpqnzml/WXXzqYZNphzuPVilA+U1AZ8jJT6mIWSN6Juq046PazTiVX8sY4bqAAAgAElEQVSb0SNO8THRHfwOG5s6PqfTBLOmggpqAACE090XDG+X6SslJSXq2bOnkpKS9Nlnn2nRokWqqKjQ+++/r6KiIqWmpuqll17S2LFjG47PyfH+wH/22WeP6bUBIFTq6pyKK2qaNNcINApWrf1lVaoLMP8tMTa6IWwNzkzWpMG9lNUjoWH0K9Pv2q+E2PCGr+6CoAYAiHjtNX1lypQp+v3vf68TTzxRw4cPV35+vrKzs/XAAw/o5JNPVnZ2tiZMmCCPxxsQH3jgAX3ta19TTk6O8vPztXnz5mN+bwAQiHNOpZW1LU419B/9KjpUrdoA6SsuJqohZPXvmaTxA3sqq8l1X/W3yfHEgkhj4bqkLC8vzy1dujQsrw0ACL9169Zp5MiR4S4jrPgeAN2Lf7v5wC3mD4987T1YFfA63Jgoa9JcI3DwykqJV0p8TMM0bEQmM1vmnMsLtI/oDAAAAByDimqP9h2q0p4Wg1f9dWDVzRZglgK3m/dvuOHfeCMtMVZRQXQ8ROdHUAMAAACaqG83vy/ANV9NR76CaTc/cWBSwFbzWSnBt5tH90JQAwCEjXOu207LYTUboOPVeOoaOh621mo+mHbzmT3iNDonrdEiy/4NN3olxyk2xO3m0b0Q1AAAYZGQkKCioiJlZGR0u7DmnFNRUZESEhLCXQoQdgtW7DimRkH17eZbm3J4uONhdcDnSImPaQhZ/u3mm173FQnt5tF9ENQAAGHRv39/FRQUaO/eveEuJSwSEhLUv3//cJcBhNWCFTsaLb2xo7hC9766Wq7O6cwRvQN2OvRvNb/3YNURtZtv1nCDdvOIYAQ1AEBYxMbGavDgweEuA0AYVNZ4tLWoXA/+ZW2z5hoVNR7d9dInAR/n324+Jz1R4wakNZtySLt5dBWcwQAAAAi5+jC2eV+ZthaVaUtRme/rcu0sqWzz8fd/ZVSz6YepCbSbR/dBUAMAAMBRqQ9jW4rKtGVfme/We79pGMtIjlNuZrJOHpKhwRnJys1M1oN/Wau9B6uaPW9OeqJuOJURd3RvBDUAAAC0qHkYK28IZYHC2KCMpIYwNigz2XebpNSE2GbP7alzja5Rk7zXlt19wfB2f19ApCOoAQAAdHOVNR5t2++dpugfxrYWlamwlTCW6xsZG5yRrIEZSUpLbB7GWlPf3fFYuj4CXRVBDQAAoBvwD2Nbi8q0eV+599qxfWXaWVop/6X9eiXHKTcjSfnHZSg30xvGcjOSNCgj+YjDWFumjs8hmAEBENQAAAC6iPowVj81sa0wNsgvjA3KSNLgzOR2CWMAjhxBDQAAoBOprPFoe/00xSL/aYrlKiypaDGMDcpIVm4mYQzoLAhqAAAAEcY/jG0tKtdm36hYoDDWMylWub4FnXN9YSw3I1m5GclKSyKMAZ0VQQ0AACAM6sNY/YjY5iLfemP7CGMACGoAAADtpmkY21J0eK2xQGFsUIY3jNVfL0YYA7ovghoAAMAxqKqtn6bYdhhLT4pVbpMwNijD296eMAbAH0ENAACgDf5hzNva3nft2L6yFsPYSbk9lZvZv2GtsdyMJKUnxYXvTQDoVAhqAAAAOhzGtuwr97W2bz2MDfKFsUEZ/b3TFAljAEKIoAYAALoNbxiraDZFcUtRmQqLK1TnF8bSEr0NPPzDWP10RcIYgPZGUAMAAF3K0YSxiYN66vIJhDEAkYOgBgAAOp3q2jpt23/4erEtRX7TFJuEsdSEGA32C2P+re17JhPGAEQmghoAAIhITcPY1qLD1461Fsa+OqG/BhPGAHRyBDUAABA21bV12n7At+CzXxjbUlSmHQcCh7EJAw+HsfrW9ulJsTKz8L0RAAgxghoAAGhX/mGs6cLPTcNYii+MjR/QU5eN76/cjCRfN8Vk9SSMAehGggpqZjZF0m8lRUt6wjn3cIBjrpT0gCQn6RPn3PQQ1gkAAMJkwYodenThehUWV6hfeqLuvmC4po7PaXRMfRjzTlM8gjA2LscbxAhjANCIOf9FQQIdYBYtaYOk8yQVSFoiaZpzbq3fMUMlvSjpHOfcATPr7Zzb09rz5uXluaVLlx5r/QAAoB0tWLFD9766WhU1noZtcdFRumhMX6UmxjZMVyw4UB4wjHmnJnpHxQZlJGtwJmEMAOqZ2TLnXF6gfcGMqE2StNE5t8n3ZPMlXSpprd8xMyU97pw7IElthTQAABA5nHMqqajR7tIq7TlY2XC7p7RK85dsU2VNXaPjqz11WrCysCGMjR2Qrqnj+mlQhndkjDAGAMcumKCWI2m73/0CSZObHDNMkszsQ3mnRz7gnHun6ROZ2SxJsyRp4MCBR1MvAAAIknNOB8prDoev0krtOei9bQhjB6u052CVqmvrmj0+JT6mWUirZ5JW3X8+YQwA2kkwQS3QJ3DT+ZIxkoZKOktSf0n/NrPRzrniRg9ybrak2ZJ36uMRVwsAAFRX53SgvLrRyJf/SNju0irtPej9usbT/MdtakKMeqcmqE9qvE7K7aXeqfHqneK973+bGBetUx/+h3YUVzR7jn7piYQ0AGhHwQS1AkkD/O73l1QY4JhFzrkaSZvNbL28wW1JSKoEAKAbqKtzKiqrbjF81Y+G7T1Ypdq65gEsPSlWvVO8Ieu4rOSA4at3arwSYqODrunuC4Y3u0YtMTZad18wPCTvGQAQWDBBbYmkoWY2WNIOSVdLatrRcYGkaZKeMbNMeadCbgploQAAdFaeOqeiQ76g1TANsUq7/QLZntIq7T1UJU+AANYzKVZ9UhOUlRKvob0zfWEsXn1SExpGw7JSjiyABau+u2NbXR8BAKHVZlBzztWa2W2SFsp7/dlTzrk1ZvagpKXOudd9+843s7WSPJLuds4VtWfhAACEW62nTkVl1dpdWh+4qrxfHzx8Pdju0krtO1SlAPlLGclxyvIFruF9UvyCV7x6pyaod0q8slLiFR8T+gB2JKaOzyGYAUAHa7M9f3uhPT8AIFLVeOq075Bv1Kv0cMMN//C152CVigIEMDNvAKufZtjHd1sfvOpHwjJ7xCsuJio8bxAAEBGOtT0/AABdQo2nTnsDjHo1nYZYVFatpr/HjDIpo8fhoDUmJ60hfPWpD2Gp8crsEa/YaAIYAODYENQAAJ1eVa3H1+WwyaiX33TEvQerVFRW3eyxUSZl+RpwZKclaOyA9Ebhq346YkZynGIIYACADkJQAwBErMoaT0Ob+abTEOvD1+7SSh0or2n22OgoU1aPePVJjVf/nkmaMKhnwzRE/w6IGcnxio6izTwAILIQ1AAAHa6yxtNC+/nKRlMTiwMEsJgo8zbZSE3QwF5Jysvt2az9fO+UBPVKjiOAAQA6LYIaACBkKqo9AcOXfwv63aWVKq2sbfbY2GhrCFqDM5OVf1xGo+6H9WGsZ1KcoghgAIAujqAGAGhTWVVtw/Vfuxs14fALYwerdDBAAIuLjmpoOX987x46ZUhG4yYcvhGwnkmxMiOAAQAgEdQAoNNbsGLHUS9GfKiqtnHYCjgSVqVDVc0DWHxMVEPDjeF9U3T60KxG7ejr96UlEsAAADhSBDUA6MQWrNihe19drYoajyRpR3GF7n11lSpqanVSbkaA8NW4CUd5tafZcybEegNYn5QEjeyXqjObdkD0TUdMTYghgAEA0E4IagDQidTVORWVVWtnSYV2llTq/tc/bQhp9Spq6nTvq582e2xSXLT6pCYoKyVeo3PSdM6I3s3CV+/UeKXEE8AAAAg3ghoARIi6Oqf95dXaWVzZEMQKSyq0q6TSu620QrtLqlTtqQvq+X579biG5hx9UhPUI56PfAAAOgt+agNAB3DOaX9ZtTd8FVdoV2mlCosrtaukQoUlldrl+9M0hMVFR6lvmnch5okDeyo7PVHZaQnKTvPe3vzcUu0qqWz2ejnpibp0XHDXqQEAgMhDUAOAY+QfwnaWNA5f9aFsZ0mlqmsbh7DYaPOFsESNH5jeEL4aglh6gnq10Yr+nikjGl2jJkmJsdG6+4Lh7fZ+AQBA+yOoAUArnHM6UF7jnYpYXKmdpZXaWeydjtgwLbGkUlUBQlif1AT1S0vU2P7pmjI6QdmpCY1GxDKSj309sPrujkfb9REAAEQmc86F5YXz8vLc0qVLw/LaACB5Q1hxeY1vJKyi8a3fdWJNQ1hMlC+EpSeob1qi+qUlNIyMZaclKDs9QZnJ8SzKDAAAWmVmy5xzeYH2MaIGoEtyzqmkoqZZ+PIfBdtZUqHKmsYhLDrK1DfVO/1wdE6azj+hr/r6Qll9EMvsQQgDAADti6AGoNNxzqm0olY7Sysaha/C4krt8m3bWVLZrG19dJSpT0q8stMTNapfqs4d2dtvFOxwCIsmhAEAgDAjqAGIKM45lVbWNhoFa9ScwxfKmi7UHGVSH99I2Mh+qTpnRG+/68G8o2FZKYQwAADQORDUAHQY55wOVtU2uv7LG8bq29V7Q1hZgBDWO8V77dfIvqk6e3jvRp0Rs9MSlNUjXjHRUWF6ZwAAAKFFUAMQMgcraxqFr8ZNOrzbWgphfdMSNLxvis4c1tvXpOPwNWG9UwhhAACgeyGoAQjKoaraRuHLu1hz4+Ych6pqGz3GTOqdEq++aYka2ruHTh+aqX5pieqbdrhjYu+UeMUSwgAAABohqAFQWVVtwPBV6Fu8eWdxpQ4GCGFZPeKVnZagIVk9dOrxmY3a1WenE8IAAACOFkEN6OK8Iaxxi/pdpY1D2cHK2maPy0rxhrDBmck6ZUimsn1rhfVLr5+OmKC4GEIYAABAeyCoAZ1YeXVts8WZ60OZt119hUoDhLBM30jYoIwk5R/Xy687ove2TyohDAAAIJwIakAHWLBihx5duF6FxRXql56ouy8Yrqnjc1p9TEW1p1kjjoapiL5tJRU1zR6X2SNOfdMSNKBXkiYN7qXstETvlMRU72hY79R4xcdEt9dbBQAAQAgQ1IB2tmDFDt376uqGxZd3FFfonldXae/BKp3QL7UhfDWsE+ZrVV9c3jyEZSTHKTs9Qf17ekNY37SEw8050rwhLCGWEAYAANDZEdSAdvbowvUNIa1eZU2dfvLWukbbeiXHKTstQf17Juqk3F6HOyOmekfE+qQmEMIAAAC6CYIa0M4Kiyta3PfCzMkNI2KEMAAAANQjqAHtLD0pVgcCTGPMSU/UKUMyw1ARAAAAIl1Qbd3MbIqZrTezjWZ2T4D915vZXjNb6ftzc+hLBTqfP6/coQPlNYqyxtsTY6N19wXDw1MUAAAAIl6bQc3MoiU9LulCSaMkTTOzUQEO/ZNzbpzvzxMhrhPodF5bUaC7/rRS+cf10sNfHaOc9ESZvCNpP/vqmDa7PgIAAKD7Cmbq4yRJG51zmyTJzOZLulTS2vYsDOjMXl5WoLtf/kSnDMnQE9eepMS4aF150sBwlwUAAIBOIpipjzmStvvdL/Bta+pyM1tlZi+b2YBAT2Rms8xsqZkt3bt371GUC0S+F5ds190vf6LTjs/Uk9d5QxoAAABwJIIJahZgm2ty/w1Juc65EyW9K+nZQE/knJvtnMtzzuVlZWUdWaVAJ/DC4m36ziurdPrQLM25No9OjgAAADgqwQS1Akn+I2T9JRX6H+CcK3LOVfnuzpE0MTTlAZ3H84u26r7XVuvs4VmaPWMiIQ0AAABHLZigtkTSUDMbbGZxkq6W9Lr/AWaW7Xf3EkmNV/IFurhn/7NFP1jwqc4d2Vu/J6QBAADgGLXZTMQ5V2tmt0laKCla0lPOuTVm9qCkpc651yXdYWaXSKqVtF/S9e1YMxBRnvpgsx78y1qdN6qPHp8+QXExQa16AQAAALTInGt6uVnHyMvLc0uXLg3LawOh8sS/N+mhN9dpygl99T/Txys2mpAGAACA4JjZMudcXqB9wbTnBxDA7//5hR5++zNdPCZbv7l6HCENAAAAIUNQA47C4+9t1KML1+srY/vp11eOVQwhDQAAACFEUAOO0GN//1y/+tsGTR3XT7/4GiENAAAAoUdQA4LknNNv3v1cv/375/rqhBw9esVYRUcFWmYQAAAAODYENSAIzjn96m8b9D//2KivTeyvhy8/kZAGAACAdkNQA9rgnNOjC9fr/97/QlefNEA/vWyMoghpAAAAaEcENaAVzjk9/PZn+sO/Nmn65IF66NLRhDQAAAC0O4Ia0ALnnH7y5jo98cFmzcgfpAcvPUFmhDQAAAC0P4IaEIBzTg/+Za2e/nCLrj8lV/d/ZRQhDQAAAB2GoAY04ZzT/a+v0XMfbdVNpw3W9y8eSUgDAABAhyKoAX7q6px+8OdPNXfxNs064zjde+EIQhoAAAA6HEEN8Kmrc/regtWa9/F2feOsIfrOBcMJaQAAAAgLghogb0i759VVenFpgW47+3j99/nDCGkAAAAIG4Iauj1PndN3Xl6lV5YX6JtfGqo7zx1KSAMAAEBYEdTQrXnqnL790id6bcUO3XXuMH3z3KHhLgkAAAAgqKH7qvXU6VsvfqLXPynUt88fptvOIaQBAAAgMhDU0C3VeOp0559W6s1VO/XdKSP0jbOGhLskAAAAoAFBDd1OjadOd8xbobc/3aX7LhqhWWcQ0gAAABBZCGroVqpr63T7vOVauGa3vn/xSN18+nHhLgkAAABohqCGbqOq1qNb567Qu+t264GvjNL1pw4Od0kAAABAQAQ1dAuVNR79v7nL9Y/P9ujHl56gGSfnhrskAAAAoEUENXR5lTUe3fL8Mv1zw1795LLRumbyoHCXBAAAALSKoIYurbLGo5nPLdUHG/fp4a+O0dWTBoa7JAAAAKBNBDV0WRXVHt383BL954si/fzyE3Vl3oBwlwQAAAAEhaCGLqm8ulY3PbNUizYX6RdXjNXlE/uHuyQAAAAgaAQ1dDllVbW64ZklWrplv3595ThNHZ8T7pIAAACAI0JQQ5dyqKpWNzz9sZZvK9Zvrh6vS8b2C3dJAAAAwBGLCuYgM5tiZuvNbKOZ3dPKcVeYmTOzvNCVCATnYGWNrn1ysZZvK9ZjhDQAAAB0Ym0GNTOLlvS4pAsljZI0zcxGBTguRdIdkhaHukigLaWVNZrx5MdaVVCix6eP18UnZoe7JAAAAOCoBTOiNknSRufcJudctaT5ki4NcNyPJT0iqTKE9QFtKqmo0YwnFmtNYYn+75oJmjKakAYAAIDOLZigliNpu9/9At+2BmY2XtIA59xfQlgb0Kbi8mp9/YnFWrfzoH53zUSdf0LfcJcEAAAAHLNgmolYgG2uYadZlKRfS7q+zScymyVpliQNHMjCwzg2B8qqdc0Ti7Vx7yH9YcZEnT2id7hLAgAAAEIimBG1Akn+KwX3l1Todz9F0mhJ75vZFkn5kl4P1FDEOTfbOZfnnMvLyso6+qrR7e0vq9Z0X0ibc20eIQ0AAABdSjBBbYmkoWY22MziJF0t6fX6nc65EudcpnMu1zmXK2mRpEucc0vbpWJ0e/sOVWn6nEXatPeQnrwuT2cOI/QDAACga2kzqDnnaiXdJmmhpHWSXnTOrTGzB83skvYuEPC392CVps1epC1FZXr6+pN0+lBCGgAAALqeoBa8ds69JemtJtt+2MKxZx17WUBze0orNW3OIhUWV+rp6yfp5CEZ4S4JAAAAaBdBBTUg3HaXVmra7EXaVVqpZ244SZOPI6QBAACg6yKoIeLtLKnQ9DmLtae0Us/dOEl5ub3CXRIAAADQrghqiGiFxRWaNmeRig5V67mbJmvioJ7hLgkAAABodwQ1RKyCA+WaNmeRistq9PxNkzR+ICENAAAA3QNBDRFp+/5yXT17kQ5W1uiPN0/W2AHp4S4JAAAA6DAENUScrUVlmj5nsQ5V1eqFmfkanZMW7pIAAACADkVQQ0TZsq9M0+YsUmWNRy/MnKwT+hHSAAAA0P0Q1BAxNu09pGlzFqnG4/TCzHyNzE4Nd0kAAABAWBDUEBE27vGGtLo6p3kz8zW8b0q4SwIAAADChqCGsPt890FNm7NYkjR/Vr6G9iGkAQAAoHuLCncB6N7W7zqoq2cvUpQR0gAAAIB6jKghbNbtLNU1TyxWbLRp3sx8HZfVI9wlAQAAABGBETWExZrCEk2bs0jxMVH606yTCWkAAACAH4IaOtynO0o0fc5iJcVGa/6sfOVmJoe7JAAAACCiMPURHeqT7cWa8eRipSTEav6sfA3olRTukgAAAICIQ1BDh1mx7YCufepjpSfFat7MfPXvSUgDAAAAAmHqIzrEsq0HNOPJj9UrOU7zZ51MSAMAAABaQVBDu1uyZb+ufXKxslLiNX9WvnLSE8NdEgAAABDRmPqIdrV4U5FueGaJ+qYmaN6sfPVJTQh3SQAAAEDEY0QN7eajL4p0/dNLlJ2WoPmENAAAACBojKihXXy4cZ9uenaJBvRM0gsz85WVEh/ukgAAAIBOgxE1hNy/P9+rG59ZokG9kjVvFiENAAAAOFKMqCGk3l+/R7OeX6bjMpP1wsx89UqOC3dJAAAAQKfDiBpC5r3P9mjWc8t0fFYPzSOkAQAAAEeNETWExLtrd+v/zV2u4X1T9PxNk5SeREgDAAAAjhYjajhmf12zS9+Yu0wjs1P0x5smE9IAAACAY8SIGo7JO5/u1G0vrNDonDQ9d9MkpSbEhrskAAAAoNNjRA1H7c1VO3XrCys0dkC6niekAQAAACETVFAzsylmtt7MNprZPQH2/5eZrTazlWb2gZmNCn2piCSvf1KoO+av0ISB6Xr2xklKIaQBAAAAIdNmUDOzaEmPS7pQ0ihJ0wIEsRecc2Occ+MkPSLpVyGvFBFjwYodunP+Ck0c1FPP3DBJPeKZQQsAAACEUjAjapMkbXTObXLOVUuaL+lS/wOcc6V+d5MludCViEjyyrICfevFlZo8OEPP3HCSkglpAAAAQMgF87/sHEnb/e4XSJrc9CAzu1XStyTFSTonJNUhory4dLu++8oqnTokU3OuzVNiXHS4SwIAAAC6pGBG1CzAtmYjZs65x51zQyR9V9L3Az6R2SwzW2pmS/fu3XtklSKs5n+8Td99ZZVOOz5TT1xHSAMAAADaUzBBrUDSAL/7/SUVtnL8fElTA+1wzs12zuU55/KysrKCrxJhNXfxVt3z6mqdMTRLc67NU0IsIQ0AAABoT8EEtSWShprZYDOLk3S1pNf9DzCzoX53L5b0eehKRDg9/9EWfe+1T3XOiN6afe1EQhoAAADQAdq8Rs05V2tmt0laKCla0lPOuTVm9qCkpc651yXdZmbnSqqRdEDSde1ZNDrGMx9u1gNvrNW5I/vo8WvGKz6GkAYAAAB0hKBa9jnn3pL0VpNtP/T7+pshrgth9sS/N+mhN9fpghP66H+mTVBcDGujAwAAAB2F3upoZva/vtBP3/pMF47uq8emjVdsNCENAAAA6EgENTTyu/e/0M/f+UwXn5it31w1jpAGAAAAhAFBDQ3+9x+f6xd/3aBLxvbTr64cqxhCGgAAABAWBDVIkn777uf69bsbdNn4HD16xYmENAAAACCMCGrdnHNOv373cz329891+YT+euSKExUdFWiNcwAAAAAdhaDWjTnn9Mu/btD/vrdRV+b118++SkgDAAAAIgFBrZtyzumRhev1u/e/0LRJA/STqWMURUgDAAAAIgJBrRtyzulnb3+m2f/apGsmD9SPLx1NSAMAAAAiCEGtm3HO6aE31+nJDzbr2pMH6UeXnCAzQhoAAAAQSQhq3YhzTj96Y62e+c8W3XBqrn745VGENAAAACACEdS6ibo6p/tfX6PnF23VzacN1vcuHklIAwAAACIUQa0bqKtz+v6fP9ULi7fpljOO0z0XjiCkAQAAABGMoNbF1dU53ffaas1fsl3/76whuvuC4YQ0AAAAIMIR1LowT53TPa+s0kvLCnT7OcfrW+cNI6QBAAAAnQBBrYvy1Dnd/fInenX5Dt157lDdee6wcJcEAAAAIEgEtS6o1lOnb7/0iRasLNS3zhumO740NNwlAQAAADgCBLUuptZTp7te/ERvfFKouy8YrlvPPj7cJQEAAAA4QgS1LqTGU6c756/Um6t36p4LR+i/zhwS7pIAAAAAHAWCWhdRXVunO+at0Dtrdun7F4/UzacfF+6SAAAAABwlgloXUF1bp1tfWK6/rd2tH355lG48bXC4SwIAAABwDAhqnVxVrUe3zl2ud9ft0Y8uOUHXnZIb7pIAAAAAHCOCWidWWePRN/64TO+t36sfTx2tGfmDwl0SAAAAgBAgqHVSlTUe3fL8Mv1zw1799LIxmj55YLhLAgAAABAiBLVOqKLao1nPL9UHG/fp55eP0VUnEdIAAACAroSg1slUVHt007NL9NGmIj1y+Yn6Wt6AcJcEAAAAIMQIap1IeXWtbnxmiT7evF+//NpYfXVC/3CXBAAAAKAdENQ6iUNVtbrx6SVaunW/fn3VOF06LifcJQEAAABoJwS1TuBgZY1ueHqJVmwv1m+vHq+vjO0X7pIAAAAAtKOoYA4ysylmtt7MNprZPQH2f8vM1prZKjP7u5nRJz5ESitrdN1TH2vl9mL9zzRCGgAAANAdtBnUzCxa0uOSLpQ0StI0MxvV5LAVkvKccydKelnSI6EutDsqqajRtU9+rFUFJfrf6RN00ZjscJcEAAAAoAMEM6I2SdJG59wm51y1pPmSLvU/wDn3nnOu3Hd3kSS6XByjkvIazXhysdYUluj/rpmgKaP7hrskAAAAAB0kmKCWI2m73/0C37aW3CTp7UA7zGyWmS01s6V79+4Nvspupri8Wtc8uUif7Tyo3399os4/gZAGAAAAdCfBBDULsM0FPNDs65LyJD0aaL9zbrZzLs85l5eVlRV8ld3IgbJqTZ+zWBt2H9IfZkzUl0b2CXdJAAAAADpYMF0fCyT5r6rcX1Jh04PM7FxJ35N0pnOuKjTldS9Fh6p0zROLtXlfmeZcm6czhxFmAQAAgO4omBG1JZKGmtlgM/2tjZ8AACAASURBVIuTdLWk1/0PMLPxkv4g6RLn3J7Ql9n17TtUpelzvCHtyetOIqQBAAAA3VibQc05VyvpNkkLJa2T9KJzbo2ZPWhml/gOe1RSD0kvmdlKM3u9hadDAHsOVmra7EXaur9MT19/kk4bmhnukgAAAACEUVALXjvn3pL0VpNtP/T7+twQ19Vt7Cmt1LQ5i7SzpFLP3DBJ+cdlhLskAAAAAGEWVFBD+9hVUqnpcxZpd6k3pE0a3CvcJQEAAACIAAS1MNlZUqFpsxdp36FqPXvjJOXlEtIAAAAAeBHUwmBHsTekHSir1nM3TdKEgT3DXRIAAACACEJQ62Db95dr2pxFKqmo0fM3T9a4AenhLgkAAABAhCGodaBtRd6QdrCyRnNvnqwT+xPSAAAAADRHUOsgW4vKNG32IpVVe/TCzHyNzkkLd0kAAAAAIhRBrQNs3ucNaVW1Hr0wc7JO6EdIAwAAANAyglo7+2LvIU2bvUi1dU4vzMzXyOzUcJcEAAAAIMIR1NrRxj0HNW3OYjnnNG9mvob3TQl3SQAAAAA6AYJaO9mw+6Cmz1kkyTRvZr6G9iGkAQAAAAhOVLgL6Io+21WqabMXKcpM82cR0gAAAAAcGYJaiK0t9Ia0mGhvSDu+d49wlwQAAACgk2HqYwh9uqNEX39ysRJjozVvZr5yM5PDXRIAAACAToigFiKrC7whrUd8jObNzNfAjKRwlwQAAACgk2LqYwh8sr1Y059YpJSEGM2fRUgDAAAAcGwIasdo+bYD+voTi5WeFKv5s/I1oBchDQAAAMCxYerjMVi2db+ue2qJMnrEad7MfPVLTwx3SQAAAAC6AEbUjtKSLft17ZMfKyslXn+adTIhDQAAAEDIMKJ2FBZvKtINzyxR37QEzZuZrz6pCeEuCQAAAEAXwojaEfrPF/t0/dNL1C89UfNnEdIAAAAAhB5B7Qh8uHGfbnxmiQb0StS8mfnqnUJIAwAAABB6TH0M0r827NXM55ZqcGay5t48WRk94sNdEgAAAIAuiqAWhPfX79Gs55dpSFYPzb15snolx4W7JAAAAABdGEGtDf/4bLf+6/nlGtrHG9LSkwhpAAAAANoX16i14m9rd+uW55dpRHaKXrg5n5AGAAAAoEMwotaCdz7dpdvnLdeofml67sZJSkuMDXdJAAAAALoJgprPghU79OjC9SosrlDPpFgdKK/RuIHpevbGSUpNIKQBAAAA6DgENXlD2r2vrlZFjUeStL+8RmbSVXn9CWkAAOD/t3fn8XHV9f7H35+k2bsnobTpkgClUGihbVo2RVSUIltlkUU2b6GAIiiKgnrZrv5QUFQEL7K3bBUEuSBLRRYVBWlCN9pSKF1o2kLXdM2ez++POUknadJO00zOzOT1fDxizjbnfCach533fM/5HADocjHdo2ZmE81skZktNrPr2lh/rJm9a2b1ZnZm55cZX7fPWNQc0pq4S7977aOQKgIAAADQne02qJlZuqS7JZ0oaaSkc81sZKvNPpZ0saTHO7vArrCqsmqPlgMAAABAPMUyojZB0mJ3X+LutZKmSzotegN3X+bucyU1xqHGuBvUN2ePlgMAAABAPMUS1IokrYiarwiW7TEzm2JmZWZWtnbt2o7sIi6uPWGEcjLSWyzLyUjXtSeMCKkiAAAAAN1ZLEHN2ljmHTmYu9/r7qXuXlpYWNiRXcTFpDFFuvX0USrqmyOTVNQ3R7eePkqTxnQojwIAAADAXoml62OFpCFR84MlrYpPOeGZNKaIYAYAAAAgIcQyojZT0nAzKzGzTEnnSHouvmUBAAAAQPe126Dm7vWSrpQ0Q9JCSU+6+3wzu8XMTpUkMxtvZhWSzpL0BzObH8+iAQAAACCVxfTAa3d/UdKLrZbdEDU9U5FLIgEAAAAAeymmB14DAAAAALoOQQ0AAAAAEgxBDQAAAAASjLl36JFoe39gs7WSlody8F0rkLQu7CKQsji/EE+cX4g3zjHEE+cX4ilRz69h7t7mA6ZDC2qJyszK3L007DqQmji/EE+cX4g3zjHEE+cX4ikZzy8ufQQAAACABENQAwAAAIAEQ1Db2b1hF4CUxvmFeOL8QrxxjiGeOL8QT0l3fnGPGgAAAAAkGEbUAAAAACDBENQAAAAAIMEQ1KKY2UQzW2Rmi83surDrQeowswfNbI2ZvRd2LUg9ZjbEzF43s4VmNt/Mrg67JqQOM8s2s3fMbE5wft0cdk1IPWaWbmazzOwvYdeC1GNmy8xsnpnNNrOysOuJFfeoBcwsXdIHkr4kqULSTEnnuvuCUAtDSjCzYyVtlTTN3Q8Nux6kFjMbKGmgu79rZr0klUuaxP9/oTOYmUnKc/etZpYh6U1JV7v72yGXhhRiZtdIKpXU291PDrsepBYzWyap1N0T8YHX7WJEbYcJkha7+xJ3r5U0XdJpIdeEFOHu/5C0Iew6kJrcfbW7vxtMb5G0UFJRuFUhVXjE1mA2I/jhW150GjMbLOkkSfeHXQuQSAhqOxRJWhE1XyE+6ABIMmZWLGmMpP+EWwlSSXBZ2mxJayS94u6cX+hMv5H0A0mNYReClOWS/mpm5WY2JexiYkVQ28HaWMY3hgCShpn1lPS0pO+4++aw60HqcPcGdz9c0mBJE8yMS7jRKczsZElr3L087FqQ0o5x97GSTpT0reCWlIRHUNuhQtKQqPnBklaFVAsA7JHg3qGnJT3m7s+EXQ9Sk7tXSnpD0sSQS0HqOEbSqcE9RNMlfcHMHg23JKQad18V/F4j6c+K3PKU8AhqO8yUNNzMSswsU9I5kp4LuSYA2K2g2cMDkha6+x1h14PUYmaFZtY3mM6RdLyk98OtCqnC3a9398HuXqzIZ6/X3P38kMtCCjGzvKDRlswsT9KXJSVFF26CWsDd6yVdKWmGIjfiP+nu88OtCqnCzJ6Q9JakEWZWYWaTw64JKeUYSRco8k307ODnK2EXhZQxUNLrZjZXkS81X3F3WqgDSBYDJL1pZnMkvSPpBXd/OeSaYkJ7fgAAAABIMIyoAQAAAECCIagBAAAAQIIhqAEAAABAgiGoAQAAAECCIagBAAAAQIIhqAEAkpqZNUQ9lmC2mV3XifsuNrOkeN4OACC19Ai7AAAA9lKVux8edhEAAHQmRtQAACnJzJaZ2S/M7J3g54Bg+TAze9XM5ga/hwbLB5jZn81sTvBzdLCrdDO7z8zmm9lfzSwntDcFAOg2CGoAgGSX0+rSx7Oj1m129wmS7pL0m2DZXZKmuftoSY9JujNYfqekv7v7YZLGSpofLB8u6W53P0RSpaQz4vx+AACQuXvYNQAA0GFmttXde7axfJmkL7j7EjPLkPSJu+eb2TpJA929Lli+2t0LzGytpMHuXhO1j2JJr7j78GD+h5Iy3P2n8X9nAIDujBE1AEAq83am29umLTVR0w3i/m4AQBcgqAEAUtnZUb/fCqb/LemcYPrrkt4Mpl+VdIUkmVm6mfXuqiIBAGiNbwUBAMkux8xmR82/7O5NLfqzzOw/inwxeW6w7CpJD5rZtZLWSvpGsPxqSfea2WRFRs6ukLQ67tUDANAG7lEDAKSk4B61UndfF3YtAADsKS59BAAAAIAEw4gaAAAAACQYRtQAAJ3GzIrNzM2sRzD/kpldFMu2HTjWj8zs/r2pFwCAREVQAwA0M7MZZnZLG8tPM7NP9jRUufuJ7j61E+o6zswqWu37/7n7JXu7bwAAEhFBDQAQ7WFJF5iZtVp+gaTH3L2+60vqXjo6wggASC0ENQBAtGcl9Zf02aYFZtZP0smSpgXzJ5nZLDPbbGYrzOym9nZmZm+Y2SXBdLqZ/dLM1pnZEkkntdr2G2a20My2mNkSM7ssWJ4n6SVJg8xsa/AzyMxuMrNHo15/qpnNN7PK4LgHR61bZmbfN7O5ZrbJzP5oZtnt1Ly/mb1mZuuDWh8zs75R64eY2TNmtjbY5q6odZdGvYcFZjY2WO5mdkDUdg+b2U+D6ePMrMLMfmhmn0h6yMz6mdlfgmNsDKYHR72+v5k9ZGargvXPBsvfM7NTorbLCN7D4e39NwIAJCaCGgCgmbtXSXpS0oVRi78m6X13nxPMbwvW91UkbF1hZpNi2P2ligS+MZJKJZ3Zav2aYH1vRZ5t9mszG+vu2ySdKGmVu/cMflZFv9DMDpT0hKTvSCqU9KKk580ss9X7mCipRNJoSRe3U6dJulXSIEkHSxoi6abgOOmS/iJpuaRiSUWSpgfrzgq2uzB4D6dKWh/D30WS9lUkIA+TNEWRf58fCuaHSqqSdFfU9o9IypV0iKR9JP06WD5N0vlR231F0mp3j37OHAAgCRDUAACtTZV0lpnlBPMXBsskSe7+hrvPc/dGd5+rSED6XAz7/Zqk37j7CnffoEgYaubuL7j7Rx7xd0l/VdTI3m6cLekFd3/F3esk/VJSjqSjo7a5091XBcd+XlKbo0zuvjjYT427r5V0R9T7m6BIgLvW3be5e7W7vxmsu0TSbe4+M3gPi919eYz1N0q6MThmlbuvd/en3X27u2+R9LOmGsxsoCLB9XJ33+judcHfS5IelfQVM+sdzF+gSKgDACQZghoAoIUgeKyVdJqZ7SdpvKTHm9ab2RFm9npwWd4mSZdLKohh14MkrYiabxFizOxEM3vbzDaYWaUio0Gx7Ldp3837c/fG4FhFUdt8EjW9XVLPtnZkZvuY2XQzW2lmmxUJP011DJG0vJ179YZI+ijGeltb6+7VUTXkmtkfzGx5UMM/JPUNRvSGSNrg7htb7yQYafyXpDOCyzVPlPRYB2sCAISIoAYAaMs0RUbSLpD0V3f/NGrd45KekzTE3ftIukeRywV3Z7UiIaPJ0KYJM8uS9LQiI2ED3L2vIpcvNu13dw/9XKXIZYJN+7PgWCtjqKu1W4PjjXb33opcSthUxwpJQ9tp+LFC0v7t7HO7IpcqNtm31frW7+97kkZIOiKo4dhguQXH6R9931wrU4Oaz5L0lrt35G8AAAgZQQ0A0JZpko5X5L6y1u31eykyolNtZhMknRfjPp+UdJWZDQ4alFwXtS5TUpYiI3n1ZnaipC9Hrf9UUr6Z9dnFvk8ysy+aWYYiQadG0r9jrC1aL0lbJVWaWZGka6PWvaNI4Py5meWZWbaZHROsu1/S981snEUcYGZN4XG2pPOChioTtftLRXspcl9apZn1l3Rj0wp3X61Ic5XfB01HMszs2KjXPitprKSrFTSAAQAkH4IaAGAn7r5MkZCTp8joWbRvSrrFzLZIukGRkBSL+yTNkDRH0ruSnok63hZJVwX72qhI+Hsuav37itwLtyTo6jioVb2LFBlF+p2kdZJOkXSKu9fGWFu0mxUJOpskvdCqzoZg3wdI+lhShSL3x8ndn1LkXrLHJW3Rjg6aUiQ0nSKpUtLXg3W78htF7rFbJ+ltSS+3Wn+BpDpJ7yvShOU7UTVWKTI6WRJdOwAguZj77q4mAQAAycTMbpB0oLufv9uNAQAJiYdqAgCQQoJLJScrMuoGAEhSXPoIAECKMLNLFWk28pK7/yPsegAAHceljwAAAACQYBhRAwAAAIAEE9o9agUFBV5cXBzW4QEAAAAgVOXl5evcvbCtdaEFteLiYpWVlYV1eAAAAAAIlZktb28dlz4CAAAAQIIhqAEAAABAgiGoAQAAAECC4YHXAIBQ1NXVqaKiQtXV1WGXEors7GwNHjxYGRkZYZcCAEhABDUAQCgqKirUq1cvFRcXy8zCLqdLubvWr1+viooKlZSUhF0OACABcekjACAU1dXVys/P73YhTZLMTPn5+d12NBEAsHuMqAEAQtMdQ1qT7vzegWjPzlqp22cs0qrKKg3qm6NrTxihSWOKwi4LCB1BDQAAAKF4dtZKXf/MPFXVNUiSVlZW6fpn5kkSYQ2dIpm/CCCoAQCSQlj/2D788MMqKyvTXXfdFfdjAammuq5BG7bVasO2Wq3fVqsN22q0fmtt87JnZ61UdX1ji9dU1TXo+0/N0Z2vfag0M6WZlBaMQKeZKS0t8tsUGZluWm+283zkd2SZqdV8sL6915p2zKelSYqeD7a3Xc1rx/Id9ew83+K9mZSWFv3edtTaer7le4s+1u7/Ls3vLS3yu+V7a3pNy/kWtSvqb5y2Yz6Wv0vr9xtdc2dL9i8CCGoAgISX7P/YAqnA3bW9tqHd0LW+VSDbsLVW22ob2txXepqpX27mTiGtSX2j6+CBvSWXGt2DH8k9UkfzvKLmGyVXZHlDY2Owvbf8rch2je6RfQXbN897+/ONLilq+8ZGD46/o0b3VvPBesSmrbAZHZh3Dp0tA29zCAzCfMXGKjU0tvwPUFXXoNtnLEqKfzsIagCA0N38/HwtWLW53fWzPq5UbcPO37r/4E9z9cQ7H7f5mpGDeuvGUw7Z7bEnTZqkFStWqLq6WldffbWmTJmihx56SLfeeqsGDhyoAw88UFlZWZKk559/Xj/96U9VW1ur/Px8PfbYYxowYIBuuukmLV26VKtXr9YHH3ygO+64Q2+//bZeeuklFRUV6fnnn6cNPxKOu2tzdX0QtHaErqbA1Tp0rd9Wq5p2glVmepr652Wqf16m8ntmqjg/NzKdl6n+eVnNy5uW9c7OUFqa6Zifv6aVlVU77a+ob47uPm9svP8EXcKDANcU3NoKdI2unUOpPGqblvvZ8xDa9Nodx1Cr+ebQ26iW81HBdZe/tetao+cbg/Ta2BhVe1QIb6q19Xzze9nFsXa8N9fy9dvb/G+yqo1zLhER1AAACa91SNvd8j3x4IMPqn///qqqqtL48eN10kkn6cYbb1R5ebn69Omjz3/+8xozZowk6TOf+YzefvttmZnuv/9+3XbbbfrVr34lSfroo4/0+uuva8GCBTrqqKP09NNP67bbbtNXv/pVvfDCC5o0adJe1wrsSmOja1NVXVTQqtG6nUa8dgSyjdtrVdfQ9nBPTkZ6c7gq6JmlAwf0ag5d+UEg698zs3m6Z1aPDl26du0JI1qMljcd+9oTRnT475Bomkd9RAOhrjZz2cY2vwgY1DcnhGr2HEENABC63Y187epb9z9edtReHfvOO+/Un//8Z0nSihUr9Mgjj+i4445TYWGhJOnss8/WBx98ICny7Lezzz5bq1evVm1tbYtnoJ144onKyMjQqFGj1NDQoIkTJ0qSRo0apWXLlu1Vjeie6hsatXF7XRCyanYErhbha8fyjdvrdrrMq0mvrB7qH4xoDe6Xo9GD+7QbuvLzspSTmd4l77Hp8rNkbfaAxJbsXwQQ1AAACS9e/9i+8cYb+tvf/qa33npLubm5Ou6443TQQQdp4cKFbW7/7W9/W9dcc41OPfVUvfHGG7rpppua1zVdHpmWlqaMjIzm0YW0tDTV19fvVZ1IDbX1je2GruZLDKMuPazcXtfuvvrmZjRfRlhSkKdxw/rvCFpBIGsKXf3yMpTVo2uCV0dMGlNEMENcJPsXAQQ1AEDCi9c/tps2bVK/fv2Um5ur999/X2+//baqqqr0xhtvaP369erdu7eeeuopHXbYYc3bFxVFjjl16tS9e1NIelW1Dc2ha/22Wm3YRejasLVWW2raDuxppuZg1T8vUwfv27vF/V7Roat/Xqb65WaoR3paF79bIDkl8xcBBDUAQFKIxz+2EydO1D333KPRo0drxIgROvLIIzVw4EDddNNNOuqoozRw4ECNHTtWDQ2RkbybbrpJZ511loqKinTkkUdq6dKlnVoPwuPu2lpT327oatFgI1gXPcIbLSPdgnAVubRwSL+oxho9WzXYyMtUn5xIYw0AiGYeUs/Q0tJSLysrC+XYAIDwLVy4UAcffHDYZYSKv0H8NDa6NlfvaKyx476utkPXhm217TanyeqR1hyyWtzXlZfZ6nLDSPjqnd2xxhoAuh8zK3f30rbWMaIGAAASXkOja+P29kNX61Gwjdtr222skZeZ3hy69u2TrZGDercMX60CWW5mOsELQJcjqAEAgF16dtbKTr8/sK6hURtbPSR5/da27veKLKusqmv3wcG9s3sov2dkNGtofq7GDO3bbujqn5ep7IzEbawBAE0IagCA0Lh7tx2pCOvWgz317KyVLTpurqys0vXPzJOkFmGtuq6h3ed17fQQ5a012lzddmMNM6lf7o5QdeCAXi0uMezfMyuqjXym+uVlKoPGGgBSEEENAJJcPEY7ukJ2drbWr1+v/Pz8bhfW3F3r169XdnZ2u+sbGl0N7nLXjulGqcFdje5qDJY1euR+rIbGYHmwrCFY5h55TWS6aTup0Vu9pmnfwfqm6Zufn79T04yqugb98Om5eujfyyLdDbfWaltt2401eqSZ+kUFrUOaLzPMavXsrsjvvrmZSqexBgDEFtTMbKKk30pKl3S/u/+81fqLJd0uaWWw6C53v78T6wQAtCHW0Y5YedSH/KYP8M0f7INlDcGH+pYf9LUjCHhUQGgOG66G4DVNAcMbcpSzdZ0+XvVJcPDoXx5VU1R9URM7bddqInq8ylv+Twzb7sF+dxTTYl37tbsaJVVsqtcfyiu1qWbRTn/HZFBT36je2T1Ukp8bubSwZxsNNvKy1DuHxhoA0BG77fpoZumSPpD0JUkVkmZKOtfdF0Rtc7GkUne/MtYD0/URADrG3bVmS42WrN2mKx4tV2XVzg/FTU8zDeqbHRklaWOkpc3QFYzepII0k9LMlJZmSjeLzKeZ0tMssjxY1jyfpmC7Ha+xYH16msnMlN56n2mR+Tb3GSxLD44bmW77NelpO/abZkEdTa9Ja7nfpjqap4PXtNi21XtucYz29tnGe27a77n3va1PN9fs9Dcu6pujf133hRD+6wJA6tjbro8TJC129yXBzqZLOk3Sgl2+CgCwVzZuq9XS9du0dO02LV23rXl62fpt2t7OZWZNGhpd44f1Dz6EtxNaogJBi9DS9JqogLFzaIls0zQdW2hpvV1boaVVwGhz37sIGME0Izid5/oTD24xaitJORnpuvaEESFWBQCpL5agViRpRdR8haQj2tjuDDM7VpHRt++6+4rWG5jZFElTJGno0KF7Xi0ApJhtNfWRELZum5atiwpk67apcvuOkbL0NNOQfjkqLsjTEfv1134FeSouyNP3n5rT7mjHHWcf3pVvBSmq6RLaZLwPEgCSWSxBra2vJVtfHPO8pCfcvcbMLpc0VdJO10O4+72S7pUilz7uYa0AkJRq6hv08frtOwLZ+m1aEoySrdnSMmQN7JOtkoI8nTRqoEoK8lQSBLIh/XKV2WPnznaMdqArTBpTRDADgC4WS1CrkDQkan6wpFXRG7j7+qjZ+yT9Yu9LA4Dk0dDoWrmxSkvWbW0eGVsShLKVG6sU3R8iPy9TxQV5OvbAwuYwVlKQp+L8POVk7tnznRjtAAAgNcUS1GZKGm5mJYp0dTxH0nnRG5jZQHdfHcyeKmlhp1YJAAnA3fXp5po2Rsa2asWGKtU2NDZv2zOrh0oK8jRmSD99dcxg7Rc1OtYnJ6NT62K0AwCA1LPboObu9WZ2paQZirTnf9Dd55vZLZLK3P05SVeZ2amS6iVtkHRxHGsGgLjauK02MhrWdM9YVDCLbuKR2SNNJfl5OmCfnvrSyH1VUpCrkoKeKinIU0HPTBpaAACADttte/54oT0/gDC11cSj6VLFtpp4NI2GRUbGeqq4IFeD+uQojQfzAgCADtrb9vwAkJRaN/GI/mndxGNQn2wVt2riUVKQpyH9c5WRvnMTDwAAgHgiqAFIatFNPJpGx3bVxKMkqolHU4v7jjTxAAAAiCeCGoCE17qJx9J1W7V03XYtXbdVH2/YrrqGHWmsV1YPFQdNPE4fM7hFi/vObuIBAAAQLwQ1AAmjqYlH6/vGlrfTxGP4Pr30pZH7No+M0cQDAACkCoIagC4V3cQj+lLFpeu2aVNV2008jtovf0dHxcI8DeydTRMPAACQ0ghqADpdUxOPJa07KrbTxKOkME8nj6aJBwAAQBOCGoAOqW9o1MrKqjZHxlZVtmziUdAzU8X5efrcgYU7WtwX5mlYf5p4AAAAtIWgBqBdTU08lqzbqmVB846mYNZWE4+SwjyNHdpPZ4wdrP0KI90UaeIBAACw5whqQDfn7tq4va5FR8Vl63ZctlhVt6OJR1aPNBW3auJREgQymngAAAB0HoIa0E1sralvvl+s9U/rJh5D++eqOD830sSjME8l+Xk08QAAAOhCBDUghUQ38Wh939jadpp4nHLYQBXn52m/wjyVFPTU4H45NPEAAAAIGUENSDJNTTyiOyo2/aysrJK30cTjuAMLW4yM0cQDAAAgsRHUgC7w7KyVun3GIq2qrNKgvjm69oQRmjSmqN3to5t4tH7484p2mniMG7ajiUdJ8ADo3tk08QAAAEhGBDUgzp6dtVLXPzOvuSnHysoqXf/MPLm7Pjdin6CTYnRHxe1tNvEoKcjTgfv00gmH7NvieWP5eTTxAAAASDUENSDObp+xqEXokqSqugZd89ScFpcpNjXxKCnIa27isV8wMkYTDwAAgO6FoAbE2arKqjaXu0v/ffJIlRTk0sQDAAAALRDUgDgb2Ddbqyqrd1pe1DdHkz9TEkJFAAAASHR8fQ/E2ciBvXdalpORrmtPGBFCNQAAAEgGBDUgjp55t0J/W7hGR+3XX0V9s2WKjKTdevqoXXZ9BAAAQPfGpY9AnJQt26Drnp6no/bL17TJE7j/DAAAADHjkyMQBys2bNdlj5RrUN9s/e/5YwlpAAAA2CN8egQ62ZbqOl0ytUx1DY164OLx6pubGXZJAAAASDJc+gh0ooZG19XTZ2vx2q2a+o0J2r+wZ9glAQAAIAkxogZ0oltfXKjX3l+jm049RJ8ZXhB2OQAAAEhSBDWgk0x/52Pd/+ZSXXx0sS44cljY5QAAACCJEdSATvDWR+v1k2ff07EHFuonJx0cdjkAAABIcgQ1YC8tW7dNVzxWruKChjeUVgAAHHhJREFUPN113hj1oMMjAAAA9hKfKIG9sKmqTpOnzpRJeuCiUvXOzgi7JAAAAKQAuj4CHVTf0KgrH39XH2/YrkcnH6Fh+XlhlwQAAIAUQVADOuiWvyzQPz9cp9vOGK0j9ssPuxwAAACkEC59BDpg2lvLNO2t5Zpy7H762vghYZcDAACAFENQA/bQPz5Yq5ufX6AvHrSPfjjxoLDLAQAAQAoiqAF7YPGarfrW4+9q+D499dtzxyg9zcIuCQAAACmIoAbEaOO2Wk2eOlNZPdJ0/0Wl6pnFLZ4AAACIDz5pAjGorW/U5Y+Wa/Wmaj1x6ZEa3C837JIAAACQwhhRA3bD3fXfz76n/yzdoNvOGK1xw/qFXRIAAABSHEEN2I0H3lyqP5at0JWfP0CTxhSFXQ4AAAC6AYIasAuvLvxUP3txoU48dF9d86UDwy4HAAAA3QRBDWjH+59s1lVPzNIhg3rrV187TGl0eAQAAEAXIagBbVi3tUaTHy5TXlYP3X/heOVm0ncHAAAAXYdPn0Ar1XUNuuyRcq3fVqMnLztK+/bJDrskAAAAdDMENSCKu+tHz8xT+fKNuvu8sRo9uG/YJQEAAKAb4tJHIMrv3/hIz8xaqWu+dKBOGj0w7HIAAADQTcUU1MxsopktMrPFZnbdLrY708zczEo7r0Sga7z83mrdPmORTj1skL79hQPCLgcAAADd2G6DmpmlS7pb0omSRko618xGtrFdL0lXSfpPZxcJxNt7Kzfpu3+co8OH9NVtZ46WGR0eAQAAEJ5YRtQmSFrs7kvcvVbSdEmntbHd/0i6TVJ1J9YHxN2nm6t1ydQy9cvN0L0XjlN2RnrYJQEAAKCbiyWoFUlaETVfESxrZmZjJA1x9790Ym1A3FXVNujSaWXaXF2n+y8ar3160eERAAAA4Yul62Nb14B580qzNEm/lnTxbndkNkXSFEkaOnRobBUCcdLY6Pr+n+Zo3spNuveCUo0c1DvskgAAAABJsY2oVUgaEjU/WNKqqPlekg6V9IaZLZN0pKTn2moo4u73unupu5cWFhZ2vGqgE/z21Q/1wtzV+uHEg/SlkQPCLgcAAABoFktQmylpuJmVmFmmpHMkPde00t03uXuBuxe7e7GktyWd6u5lcakY6ATPzVml3776oc4cN1iXHbtf2OUAAAAALew2qLl7vaQrJc2QtFDSk+4+38xuMbNT410g0NlmfbxR339qjiYU99fPvnooHR4BAACQcGK5R03u/qKkF1stu6GdbY/b+7KA+FhVWaVLp5VrQO8s3XPBOGX1oMMjAAAAEk9MQQ1IBdtq6jV5aplq6hr0xKVHqH9eZtglAQAAAG0iqKFbaGx0ffePs7Xok8168OLxGj6gV9glAQAAAO2KpZkIkPRu/+si/XXBp/rvk0fquBH7hF0OAAAAsEsENaS8P5VX6H/f+EjnHTFUFx9dHHY5AAAAwG4R1JDSZi7boOufmauj98/XzaceQodHAAAAJAWCGlLWig3bddkj5RrcL1e///pYZaRzugMAACA58MkVKWlLdZ0mT52p+oZGPXBRqfrm0uERAAAAyYOuj0g5DY2ubz8xSx+t3aZp/zVB+xX2DLskAAAAYI8wooaU87MXFuqNRWt1y2mH6JgDCsIuBwAAANhjBDWklMf/87Ee/NdSXXx0sb5+xLCwywEAAAA6hKCGlPHvj9bphv97T587sFA/OengsMsBAAAAOoyghpSwdN02XfHouyopyNPvzhujHnR4BAAAQBLj0yyS3qbtdZr88EylmfTARePVOzsj7JIAAACAvULXRyS1uoZGffPxcq3YuF2PXXKkhubnhl0SAAAAsNcIakha7q6bn5+vfy1er9vPHK0JJf3DLgkAAADoFFz6iKQ17a3levTtj3XZsfvprNIhYZcDAAAAdBqCGpLS3z9Yq5ufn6/jDx6gH0w8KOxyAAAAgE5FUEPSWbxmi6587F2N2Le3fnvO4UpPs7BLAgAAADoVQQ1JZcO2Wv3Xw2XKykjX/ReVKi+L2ywBAACQeviUi6RRW9+oyx8t1yebqzV9ypEq6psTdkkAAABAXDCihqTg7vrJs/P0ztINuv3M0Ro7tF/YJQEAAABxQ1BDUrjvn0v0ZFmFrvrCATrt8KKwywEAAADiiqCGhPe3BZ/q1pfe11dG7avvHH9g2OUAAAAAcUdQQ0JbuHqzrp4+S4cO6qNfnXW40ujwCAAAgG6AoIaEtXZLjS6ZWqae2T1034WlyslMD7skAAAAoEvQ9REJqbquQZc9Uqb122r01GVHa98+2WGXBAAAAHQZghoSjrvruqfn6t2PK/X7r4/VqMF9wi4JAAAA6FJc+oiEc/fri/Xs7FX6/pcP1FdGDQy7HAAAAKDLEdSQUF6at1q//OsHmnT4IH3r8weEXQ4AAAAQCoIaEsa8ik367pOzNWZoX/38jNEyo8MjAAAAuieCGhLCp5urdcm0mcrPy9K9F5QqO4MOjwAAAOi+aCaC0FXVNuiSqWXaUl2vp684WoW9ssIuCQAAAAgVQQ2hamx0fe+p2Xpv1Sbdd0GpDh7YO+ySAAAAgNBx6SNC9Zu/faAX532i6088SMePHBB2OQAAAEBCIKghNP83e6XufG2xvlY6WJd+dr+wywEAAAASBkENoXj344269k9zNaGkv346aRQdHgEAAIAoBDV0uZWVVZoyrVz79s7WPeePU2YPTkMAAAAgGs1E0KW21dRr8sMzVVPXoCcuPUL98zLDLgkAAABIOAQ1dJmGRtfV02frg0+36KFvTNDwAb3CLgkAAABISFxzhi5z24z39beFn+qGk0fqcwcWhl0OAAAAkLAIaugST5Wt0B/+vkTnHzlUFx1dHHY5AAAAQEIjqCHu3lm6QT/68zwdc0C+bjzlEDo8AgAAALtBUENcfbx+uy57pExD+uXq9+eNU0Y6pxwAAACwOzF9ajaziWa2yMwWm9l1bay/3MzmmdlsM3vTzEZ2fqlINpur6zR56kw1uvTAxePVJzcj7JIAAACApLDboGZm6ZLulnSipJGSzm0jiD3u7qPc/XBJt0m6o9MrRVKpb2jUtx+fpaXrtul/zx+rkoK8sEsCAAAAkkYsI2oTJC129yXuXitpuqTTojdw981Rs3mSvPNKRDL62YsL9fcP1uqW0w7V0fsXhF0OAAAAkFRieY5akaQVUfMVko5ovZGZfUvSNZIyJX2hU6pDUnrsP8v10L+W6b+OKdF5RwwNuxwAAAAg6cQyotZWi76dRszc/W5331/SDyX9pM0dmU0xszIzK1u7du2eVYqk8O/F63TD/83XcSMK9eOTDg67HAAAACApxRLUKiQNiZofLGnVLrafLmlSWyvc/V53L3X30sJCHnicapas3arLHy3XfgV5+t25Y5SeRht+AAAAoCNiCWozJQ03sxIzy5R0jqTnojcws+FRsydJ+rDzSkQy2LS9TpOnlqlHepoevHi8emXT4REAAADoqN3eo+bu9WZ2paQZktIlPeju883sFkll7v6cpCvN7HhJdZI2SroonkUjsdQ1NOqKx8pVsXG7Hr/0SA3pnxt2SQAAAEBSi6WZiNz9RUkvtlp2Q9T01Z1cF5KEu+vG5+br3x+t1y/POkzji/uHXRIAAACQ9GJ64DXQnof/vUyP/+djXf65/XXmuMFhlwMAAACkBIIaOuz1RWv0P39ZoC+PHKAfnDAi7HIAAACAlEFQQ4d88OkWffvxWRqxb2/9+uzDlUaHRwAAAKDTENSwx9ZvrdHkqTOVnZGuBy4qVV5WTLc6AgAAAIgRn7CxR2rqG3T5o+X6dHON/jjlSA3qmxN2SQAAAEDKYUQNMXN3/fjP72nmso365VmHaczQfmGXBAAAAKQkghpidu8/luhP5RW66ovDdephg8IuBwAAAEhZBDXE5JUFn+rnL7+vk0YP1He+ODzscgAAAICURlDDbi1YtVlXT5+lUUV99MszD6PDIwAAABBnBDXs0pot1bpk6kz1zs7QfReWKiczPeySAAAAgJRH10e0q7quQVOmlWvj9jo9dflRGtA7O+ySAAAAgG6BoIY2ubt+8Ke5mr2iUvecP1aHFvUJuyQAAACg2+DSR7TprtcW67k5q3TtCSM08dCBYZcDAAAAdCsENezkhbmr9atXPtBXxxTpm8ftH3Y5AAAAQLdDUEMLcysq9b2nZmvcsH669fRRMqPDIwAAANDVCGpo9smmal0ytUz5eVn6wwXjlJ1Bh0cAAAAgDAQ1SJK219brkmkzta2mXg9cXKqCnllhlwQAAAB0W3R9hBobXd97co7mr9qs+y8s1UH79g67JAAAAKBbY0QNuuOVD/TSe5/ox185WF88eEDY5QAAAADdHkGtm3t21krd9fpinV06RJM/UxJ2OQAAAABEUOvWypdv1A+enqsjSvrrfyYdSodHAAAAIEEQ1Lqpio3bddkjZRrYJ1v3nD9OmT04FQAAAIBEQTORbmhrTb0umVqmmvpGTZ8yXv3yMsMuCQAAAEAUglo309DouvqJWfpwzVY9dPF4HbBPz7BLAgAAANAK17t1M794+X29+v4a3XjKSB17YGHY5QAAAABoA0GtG3ly5grd+48luuDIYbrwqOKwywEAAADQDoJaN/H2kvX68bPz9JkDCnTjKSPDLgcAAADALhDUuoHl67fpikfLNaR/ru7++lj1SOc/OwAAAJDI+MSe4jZX12ny1DI1uvTgRePVJycj7JIAAAAA7AZBLYXVNzTqW4+9q2Xrtume88epuCAv7JIAAAAAxID2/Cnspy8s1D8/XKefnz5KR+2fH3Y5AAAAAGLEiFqKeuTt5Xr438s0+TMlOmfC0LDLAQAAALAHCGop6M0P1+mm5+br8yMK9aOvHBx2OQAAAAD2EEEtxXy0dqu++Vi5DijsqTvPHaP0NAu7JAAAAAB7iKCWQiq312rywzOVkZ6m+y8qVa9sOjwCAAAAyYhmIimirqFRVzz6rlZVVuvxS4/QkP65YZcEAAAAoIMIainA3XXD/72nt5as1x1fO0ylxf3DLgkAAADAXuDSxxTw4L+W6Yl3Vuibx+2v08cODrscAAAAAHuJoJbkXn9/jX72wgKdcMgAff/LI8IuBwAAAEAnIKglsUWfbNG3n5ilg/btrV+ffbjS6PAIAAAApASCWpJav7VGk6fOVE5muh64uFS5mdxuCAAAAKQKgloSqqlv0GWPlGvtlhrdd2GpBvbJCbskAAAAAJ2IYZgk4+66/pl5Klu+Ub87d4wOH9I37JIAAAAAdLKYRtTMbKKZLTKzxWZ2XRvrrzGzBWY218xeNbNhnV8qJOmevy/RM++u1HeOH65TDhsUdjkAAAAA4mC3Qc3M0iXdLelESSMlnWtmI1ttNktSqbuPlvQnSbd1dqGQZsz/RLfNeF8njx6oq784POxyAAAAAMRJLCNqEyQtdvcl7l4rabqk06I3cPfX3X17MPu2JB7m1cneW7lJ35k+W6OL+uiXZx0mMzo8AgAAAKkqlqBWJGlF1HxFsKw9kyW9tDdFoaU1m6t16bQy9c3N0H0Xlio7Iz3skgAAAADEUSzNRNoauvE2NzQ7X1KppM+1s36KpCmSNHTo0BhL7N6q6xp06SPlqtxep6cuP0r79M4OuyQAAAAAcRbLiFqFpCFR84MlrWq9kZkdL+nHkk5195q2duTu97p7qbuXFhYWdqTebsXdde2f5mrOikr9+uzDdWhRn7BLAgAAANAFYglqMyUNN7MSM8uUdI6k56I3MLMxkv6gSEhb0/lldk93vrpYz89ZpR9MHKGJh+4bdjkAAAAAushug5q710u6UtIMSQslPenu883sFjM7Ndjsdkk9JT1lZrPN7Ll2docY/WXuKv36bx/o9LFFuuJz+4ddDgAAAIAuFNMDr939RUkvtlp2Q9T08Z1cV7c2Z0WlvvfkHJUO66dbTx9Fh0cAAACgm4npgdfoOqs3VenSaWUq7JWlP1wwTlk96PAIAAAAdDcEtQSyvbZel0wt0/baBj1w0Xjl98wKuyQAAAAAISCoJYjGRtc1f5yjhas363fnjtGIfXuFXRIAAACAkBDUEsSvXlmkl+d/oh995WB9/qB9wi4HAAAAQIgIagngmXcrdPfrH+ncCUM0+TMlYZcDAAAAIGQEtZCVL9+g656epyP366+bTz2UDo8AAAAACGphWrFhu6ZMK9egvtn636+PU2YP/nMAAAAAIKiFZkt1nS6ZWqbahkbdf9F49cvLDLskAAAAAAkipgdeo3M1NLqunj5bi9du1cPfGK8D9ukZdkkAAAAAEggjaiG49cWFeu39NbrplJH67PDCsMsBAAAAkGAIal1s+jsf6/43l+qio4bpgqOKwy4HAAAAQAIiqHWhtz5ar588+54+O7xA/33yyLDLAQAAAJCgCGpdZNm6bbrisXINy8/VXeeNVY90/vQAAAAA2kZa6AKbquo0eepMSdIDF41Xn5yMkCsCAAAAkMgIanFW39CoKx9/V8vXb9c9549TcUFe2CUBAAAASHC054+zW/6yQP/8cJ1+ccYoHblfftjlAAAAAEgCjKjF0bS3lmnaW8t16WdLdPb4oWGXAwAAACBJENTi5J8frtXNzy/QFw/aR9edeHDY5QAAAABIIgS1OFi8Zqu++di7Gr5PT/323DFKT7OwSwIAAACQRAhqnWzjtlpNnjpTWT3SdP9FpeqZxW2AAAAAAPYMKaIT1dY36vJHy7W6slpPTDlCg/vlhl0SAAAAgCREUOsk7q7/fvY9/WfpBv367MM0blj/sEsCAAAAkKS49LGTPPDmUv2xbIW+9fn99dUxg8MuBwAAAEASI6h1glcXfqqfvbhQEw/ZV9/70oiwywEAAACQ5Ahqe+n9Tzbrqidm6ZBBvXXH2YcpjQ6PAAAAAPYSQW0vrNtao8kPlykvq4fuu7BUuZnc8gcAAABg75EsOqi6rkGXPVKudVtr9ORlR2lgn5ywSwIAAACQIghqHeDu+tEz81S+fKPuOm+MDhvSN+ySAAAAAKQQLn3sgN+/8ZGembVS3z3+QJ08elDY5QAAAABIMQS1PfTye6t1+4xFOuWwQbrqiweEXQ4AAACAFERQ2wPvrdyk7/5xjg4f0le3nzlaZnR4BAAAAND5CGoxWrO5WpdMLVO/3Azde+E4ZWekh10SAAAAgBRFM5EYVNc16NJpZdpcXaenLj9K+/TKDrskAAAAACmMoLYb7q7vPTVHc1du0j3nj9Mhg/qEXRIAAACAFMelj7vxm799qBfmrtYPTjhIJxyyb9jlAAAAAOgGCGq78NycVfrtqx/qjLGDdfnn9gu7HAAAAADdBEGtHbM+3qjvPzVH44v76f+dfigdHgEAAAB0GYJaG1ZVVunSaeUa0DtL95w/Tlk96PAIAAAAoOvQTCTw7KyVun3GIq2qrFJ6mindpMcv/azye2aFXRoAAACAboYRNUVC2vXPzNPKyiq5pPpGl8u0YNXmsEsDAAAA0A0R1CTdPmORquoaWiyrbWjU7TMWhVQRAAAAgO6MoKbIPWl7shwAAAAA4omgJmlQ35w9Wg4AAAAA8RRTUDOziWa2yMwWm9l1baw/1szeNbN6Mzuz88uMr2tPGKGcjJadHXMy0nXtCSNCqggAAABAd7bboGZm6ZLulnSipJGSzjWzka02+1jSxZIe7+wCu8KkMUW69fRRKuqbI5NU1DdHt54+SpPGFIVdGgAAAIBuKJb2/BMkLXb3JZJkZtMlnSZpQdMG7r4sWNcYhxq7xKQxRQQzAAAAAAkhlksfiyStiJqvCJYBAAAAAOIglqBmbSzzjhzMzKaYWZmZla1du7YjuwAAAACAlBdLUKuQNCRqfrCkVR05mLvf6+6l7l5aWFjYkV0AAAAAQMqLJajNlDTczErMLFPSOZKei29ZAAAAANB97TaouXu9pCslzZC0UNKT7j7fzG4xs1MlyczGm1mFpLMk/cHM5sezaAAAAABIZbF0fZS7vyjpxVbLboianqnIJZEAAAAAgL1k7h3qC7L3BzZbK2l5KAfftQJJ68IuAimL8wvxxPmFeOMcQzxxfiGeEvX8GububTbvCC2oJSozK3P30rDrQGri/EI8cX4h3jjHEE+cX4inZDy/YmkmAgAAAADoQgQ1AAAAAEgwBLWd3Rt2AUhpnF+IJ84vxBvnGOKJ8wvxlHTnF/eoAQAAAECCYUQNAAAAABIMQQ0AAAAAEgxBLYqZTTSzRWa22MyuC7sepA4ze9DM1pjZe2HXgtRjZkPM7HUzW2hm883s6rBrQuows2wze8fM5gTn181h14TUY2bpZjbLzP4Sdi1IPWa2zMzmmdlsMysLu55YcY9awMzSJX0g6UuSKiTNlHSuuy8ItTCkBDM7VtJWSdPc/dCw60FqMbOBkga6+7tm1ktSuaRJ/P8XOoOZmaQ8d99qZhmS3pR0tbu/HXJpSCFmdo2kUkm93f3ksOtBajGzZZJK3T0RH3jdLkbUdpggabG7L3H3WknTJZ0Wck1IEe7+D0kbwq4DqcndV7v7u8H0FkkLJRWFWxVShUdsDWYzgh++5UWnMbPBkk6SdH/YtQCJhKC2Q5GkFVHzFeKDDoAkY2bFksZI+k+4lSCVBJelzZa0RtIr7s75hc70G0k/kNQYdiFIWS7pr2ZWbmZTwi4mVgS1HayNZXxjCCBpmFlPSU9L+o67bw67HqQOd29w98MlDZY0wcy4hBudwsxOlrTG3cvDrgUp7Rh3HyvpREnfCm5JSXgEtR0qJA2Jmh8saVVItQDAHgnuHXpa0mPu/kzY9SA1uXulpDckTQy5FKSOYySdGtxDNF3SF8zs0XBLQqpx91XB7zWS/qzILU8Jj6C2w0xJw82sxMwyJZ0j6bmQawKA3QqaPTwgaaG73xF2PUgtZlZoZn2D6RxJx0t6P9yqkCrc/Xp3H+zuxYp89nrN3c8PuSykEDPLCxptyczyJH1ZUlJ04SaoBdy9XtKVkmYociP+k+4+P9yqkCrM7AlJb0kaYWYVZjY57JqQUo6RdIEi30TPDn6+EnZRSBkDJb1uZnMV+VLzFXenhTqAZDFA0ptmNkfSO5JecPeXQ64pJrTnBwAAAIAEw4gaAAAAACQYghoAAAAAJBiCGgAAAAAkGIIaAAAAACQYghoAAAAAJBiCGgAgqZlZQ9RjCWab2XWduO9iM0uK5+0AAFJLj7ALAABgL1W5++FhFwEAQGdiRA0AkJLMbJmZ/cLM3gl+DgiWDzOzV81sbvB7aLB8gJn92czmBD9HB7tKN7P7zGy+mf3VzHJCe1MAgG6DoAYASHY5rS59PDtq3WZ3nyDpLkm/CZbdJWmau4+W9JikO4Pld0r6u7sfJmmspPnB8uGS7nb3QyRVSjojzu8HAACZu4ddAwAAHWZmW929ZxvLl0n6grsvMbMMSZ+4e76ZrZM00N3rguWr3b3AzNZKGuzuNVH7KJb0irsPD+Z/KCnD3X8a/3cGAOjOGFEDAKQyb2e6vW3aUhM13SDu7wYAdAGCGgAglZ0d9futYPrfks4Jpr8u6c1g+lVJV0iSmaWbWe+uKhIAgNb4VhAAkOxyzGx21PzL7t7Uoj/LzP6jyBeT5wbLrpL0oJldK2mtpG8Ey6+WdK+ZTVZk5OwKSavjXj0AAG3gHjUAQEoK7lErdfd1YdcCAMCe4tJHAAAAAEgwjKgBAAAAQIJhRA0AAAAAEgxBDQAAAAASDEENAAAAABIMQQ0AAAAAEgxBDQAAAAASzP8H3LLek0UpDCsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = None\n",
    "################################################################################\n",
    "# TODO: Train the best FullyConnectedNet that you can on CIFAR-10. You might   #\n",
    "# batch normalization and dropout useful. Store your best model in the         #\n",
    "# best_model variable.                                                         #\n",
    "################################################################################\n",
    "weight_scale = 2.5e-2\n",
    "learning_rate = 3.1e-4\n",
    "update_rule='adam'\n",
    "model = FullyConnectedNet([600, 500, 400, 300, 200], weight_scale=weight_scale)\n",
    "\n",
    "solver = Solver(model, data,\n",
    "                print_every=500, num_epochs=5, batch_size=100,\n",
    "                update_rule='rmsprop',\n",
    "                optim_config={\n",
    "                'learning_rate': learning_rate\n",
    "                },\n",
    "                lr_decay=0.9,\n",
    "                verbose=True)\n",
    "solver.train()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.plot(solver.loss_history, label=update_rule)\n",
    "  \n",
    "plt.subplot(3, 1, 2)\n",
    "plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()\n",
    "\n",
    "best_model = model\n",
    "\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 测试你的模型\n",
    "在验证和测试集上运行您的最佳模型。验证集的准确率应达到50%以上。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation set accuracy:  0.53\n",
      "Test set accuracy:  0.555\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = np.argmax(best_model.loss(data['X_test']), axis=1)\n",
    "y_val_pred = np.argmax(best_model.loss(data['X_val']), axis=1)\n",
    "print('Validation set accuracy: ', (y_val_pred == data['y_val']).mean())\n",
    "print('Test set accuracy: ', (y_test_pred == data['y_test']).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
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